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HTA Report: Breast cancer risk prediction model for Health Risk Assessment (HRA) module

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Breast Cancer Risk Prediction Model For Health Risk Assessment (HRA) Module

Health Technology Assessment Report

DISCLAIMER

This Health Technology Assessment has been developed from analysis, interpretation and synthesis of scientific research and/or technology assessment conducted by other organizations. It also incorporates, where available, Malaysian data, and information provided by experts to the Ministry of Health Malaysia. While effort has been made to do so, this document may not fully reflect all scientific research available. Additionally, other relevant scientific findings may have been reported since completion of the review.

Please contact: [email protected], if you would like further information.

Published by Malaysian Health Technology Assessment Section, (MaHTAS)Medical Development Division, Ministry of Health MalaysiaLevel 4, Block E1, Complex E, Precinct 1Federal Government Administrative Centre62590, Putrajaya, MalaysiaTel: 603 88831246Fax: 603 8883 1230

CopyrightThe copyright owner of this publication is the Malaysian Health Technology Assessment Section (MaHTAS), Medical Development Division, Ministry of Health Malaysia. Content may be reproduced in any number of copies and in any format or medium provided that a copyright acknowledgement to the Malaysian Health Technology Assessment Section (MaHTAS) is included and the content is not changed, not sold, nor used to promote or endorse any product or service, and not used in an inappropriate or misleading context.

ISBN: 978-967-0769-30-1

Available on the MOH website: http://www.moh.gov.my/v/hta

This Health Technology Assessment report was issued in June 2015.


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AUTHORS

Dr. Roza Sarimin(Public Health Physician)Senior Principal Assistant DirectorHealth Technology Assessment Section (MaHTAS)Medical Development DivisionMinistry of Health Malaysia

Dr. Izzuna Mudla Mohamed Ghazali(Public Health Physician)Head of Health Technology Assessment UnitHealth Technology Assessment Section (MaHTAS)Medical Development Division Ministry of Health Malaysia

Dr. Khadijah Abdul RahimPrincipal Assistant Director Health Technology Assessment Section (MaHTAS)Medical Development DivisionMinistry of Health Malaysia

INFORMATION SPECIALIST

Dr. Nur Farhana MohamadAssistant Director /Information Specialist Health Technology Assessment Section (MaHTAS)Medical Development DivisionMinistry of Health Malaysia

EXPERT COMMITTEE

Dr. Nor Aina Emran Consultant Surgeon Breast and Endocrine Surgical UnitDepartment of SurgeryHospital Kuala Lumpur

Dr. Anita BaghawiConsultant Breast & Endocrine Surgeon Department of SurgeryHospital Putrajaya

Dr. Normayah KitanConsultant Breast & Endocrine Surgeon Department of SurgeryHospital Putrajaya


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Dr. Keng Wee TeikConsultant Clinical GeneticistDepartment of GeneticHospital Kuala Lumpur

Dr. Ch’ng Gaik SiewConsultant Clinical GeneticistDepartment of GeneticHospital Kuala Lumpur

Dr. Suzaini Mat DaudFamily Medicine SpecialistKlinik Kesihatan Arau, Perlis

Dr. Nor Saleha bt. Ibrahim TaminPublic Health PhysicianCancer UnitDisease Control DivisionMinistry of Health Malaysia

Dr. Zaleha A.HamidPublic Health PhysicianAdult Health UnitFamily Health Development DivisionMinistry of Health Malaysia

En. Azman MohammedPrincipal Assistant DirectorHealth Education DivisionMinistry of Health Malaysia

Pn. Norhaslida Mohd NorAssistant DirectorHealth Education DivisionMinistry of Health Malaysia

Datin Dr. Rugayah BakriPublic Health PhysicianHead of Health Technology Assessment Section (MaHTAS)Deputy Director, Medical Development Division Ministry of Health Malaysia

Dr. Junainah SabirinPublic Health PhysicianHead of Health Technology Policy UnitHealth Technology Assessment Section (MaHTAS)Medical Development Division Ministry of Health Malaysia


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EXTERNAL REVIEWERS

Dato’ Dr. Cheng-Har YipConsultant Breast SurgeonBreast Centre, Sime Darby Medical Centre

Professor Dr. Nur Aishah TaibConsultant Breast SurgeonHead of Breast Unit, Division of General Surgery, Faculty of Medicine,Head of Breast Cancer Resource Centre, University of Malaya Medical Centre,University of Malaya, Kuala Lumpur

Professor Dr. Teo Soo HwangChief Executive and Head of Breast Cancer Research Programme, Cancer Research Initiative Foundation (CARIF)Adjunct Professor, University MalayaFellow of Academy of Sciences Malaysia

ACKNOWLEDGEMENT

The authors for this Health Technology Assessment Report would like to express their gratitude and appreciation to the following for their contribution and assistance:

• Health Technology Assessment and Clinical Practice Guidelines Council.• Technical Advisory Committee for Health Technology Assessment.

DISCLOSURE

The authors of this report have no competing interest in this subject and the preparation of this report is totally funded by the Ministry of Health, Malaysia.

EXECUTIVE SUMMARY BackgroundBreast cancer is the commonest cancer among women and the commonest cause of cancer death worldwide. Similarly in Malaysia, it was reported to be the commonest cancer in women with overall age standardized incidence rate (ASR) of 29.1 per 100,000 population (National Cancer Registry 2007), higher than incidence in other developing countries (ASR of 20 per 100,000 population). About 30 to 40% of Malaysian women presented at a later stage of breast cancer (stage III and IV). Hence, affordable and effective approaches in cancer control are needed for early detection, diagnosis and treatment of breast cancer particularly in the less developed countries. The growing public awareness of breast cancer and its risk factors, with availability of medical and surgical risk reduction options has led consultation of many women on their breast cancer risk. Considerable effort has been directed at identifying risk factors and developing risk prediction models for breast cancer. Prediction model is a mathematical equation designed to quantify the risk an individual woman would develop a particular cancer in a defined period. It provides an estimation of disease risk that can be used to guide management for women at all level of risks. Multiple prediction models have been developed to assist with breast cancer risk prediction efforts. Reliable accurate prediction models can inform future disease burdens, health policies, individual decisions on future screening behaviour and adoption of risk reduction strategies, counsel those at risk, design prevention strategies for at risk populations and plan intervention trials. Subsequently, the adoption of such models should guide decision-making, improve patient outcomes and the cost-effectiveness of care. Given the variance in breast cancer risk, surveillance and primary prevention adapted to individual risk level may be the most effective use of resources for preventing, detecting and improving breast cancer survival.


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In Malaysia, currently there is no breast cancer risk assessment/prediction model in the prediction of individual risks of developing breast cancer in existing health risk assessment (HRA) module of Ministry of Health. An opportunistic screening policy is currently being practiced for breast cancer in Malaysia by means of both primary and secondary prevention approach as part of cancer control strategy. Therefore, there is a need to assess the feasibility of having a risk assessment/prediction model as a HRA module in enhancing early detection of breast cancer by capturing wider coverage of general population. Introduction of a risk assessment/risk prediction model for breast cancer is timely in addressing unmet needs of identifying high risk individuals in the Malaysian community, towards enhancing early detection of breast cancer in facilitating more effective cancer control strategies. This assessment was requested by a Senior Principal Assistant Director, Health Education Division, Ministry of Health.

Technical featuresA risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a future condition, such as breast cancer, within a certain time. It uses multiple predictors (covariates) to estimate the probability or risk that a certain outcome is present. The goal of risk prediction is to provide individualised risk (absolute risk) with associated measures of uncertainty thus stratifying individuals by these risks. It is accomplished by combining the baseline risk of developing the condition with an individual’s risk score. The baseline risk of the condition represents the underlying population risk for patients whose risk factor values are not present, which is usually estimated from a prospective population-based cohort study. The risk-score component shows how much the baseline risk is multiplied for increasing values of the risk factors, which may also be estimated using a cohort study or for rare conditions, a case-control study. The variables in the model can be any combination of environmental, behavioural, genetic or psychological attributes of the person. Developed models need to provide accurate and validated (internally and externally; temporal, geographical and domain/setting) estimates of disease probabilities. Performance of predictive tests is commonly measured by means of calibration (the ability to predict the number of events in subgroups of the population) and discrimination (the ability to distinguish at the individual level between those who will develop the disease and those who will not). Calibration performance is commonly reported by E/O statistics comparing expected (E) and observed (O) number of events, and discrimination performance by concordance (c)-statistics. Performance of the model may also vary according to the population they are applied to. Cancer risk prediction model is classified into absolute risk prediction model aimed at assessing probability that an individual with given risk factors and a given age will develop cancer over a defined period of time (such as Gail model); and gene carrier status risk prediction models aimed at assessing mutation probability of an individual or carrying a gene mutation that predisposes to a particular cancer (such as BOADICEA, BRCAPRO, Cuzick-Tyrer (IBIS models) and Manchaster Scoring System). Each model has unique attributes stemming from the methodology, sample size and population characteristics used to create the model.

Policy question i. In Ministry of Health, should a breast cancer risk prediction model for health risk assessment

(HRA) module be introduced as one of the strategies in the prevention of breast cancer under the Malaysia National Cancer Control Programme?

ii. If breast cancer risk prediction model for HRA module is to be introduced, which risk prediction model should be adopted in Malaysia?

Objectives i. To assess the effectiveness in term of predictive accuracy of breast cancer risk assessment/

prediction models among women ii. To assess the safety, organizational, ethical issues and economic implications related to risk

assessment/prediction models for breast cancer among women

MethodsStudies were identified by searching electronic databases. The following databases were searched through the Ovid interface: MEDLINE(R) In-process and other Non-Indexed Citations and Ovid MEDLINE(R) 1946 to present. EBM Reviews-Cochrane Database of Systematic Reviews (2005 to May 2015), EBM Reviews-Cochrane Central Register of Controlled Trials (May 2014), EBM Reviews – Database of Abstracts of Review of Effects (2nd Quarter 2015), EBM Reviews-Health Technology Assessment (2nd Quarter 2015), EBM Reviews-NHS Economic Evaluation Database (2nd Quarter 2015). Parallel searches were run in PubMed. Appendix 3 showed the detailed search strategies. No limits were applied to the search. The last search was run on 20 March 2015. From cross referencing


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of retrieved articles, additional articles were identified. All relevant literature was appraised using the Critical Appraisal Skills Programme (CASP) tool. All full text articles were graded based on guidelines from the U.S./Canadian Preventive Services Task Force.

Results A total of 830 titles were identified through Ovid interface, Pubmed and references of retrieved articles. A total of 123 abstracts were screened using the inclusion and exclusion criteria resulting in 58 potentially relevant articles. After the critical appraisal, only 14 full texts out of the above were finally included in this review comprising of two systematic reviews, one randomised controlled trial and eleven observational studies (eight cohorts and three cross sectional). No evidence on cost-effectiveness or cost-utility analysis was retrieved.

Of these, eleven articles were related to the effectiveness (predictive accuracy) of risk assessment/ risk prediction/ health risk assessment models for breast cancer, which were two systematic reviews and nine observational studies (eight cohort and two cross sectional). The other two articles were related to safety of risk prediction models for breast cancer. The articles were published between 2009 and 2015. The included systematic reviews were both published in 2012. Most of the observational studies were conducted in the U.S.A., with one each from Italy, Singapore, Thailand and Korea.The total pooled sample size of included studies was 437,049 subjects. Sample sizes for each of the observational studies ranged from 690 to 135,329 subjects. The length of follow-up ranged from five years to ten years.

Effectiveness (predictive accuracy) Models being assessed were Gail model (also known as Breast Cancer Risk Assessment Tool) including Gail 1 and Gail 2 (updated) model, Contraceptive and Reproductive Experience (CARE) model, model by Petracci, model by Pfeiffer, Vermont model, model by Anothaisintawee and BWHS model.

Performance of prediction model is commonly measured by means of calibration and discrimination. A well-fitted model has Expected/Observed (E/O) ratio close to 1, a number lower underestimates the condition’s incidence and a number higher overestimates the incidence. The concordance (c)-statistics measure model discrimination performance which is similar to area under the receiver operating characteristic curve (ROC). A c-statistics of 1.0 indicates perfect discrimination and 0.5 equivalents to no discrimination between people who develop the condition and those who do not.

Performance of Gail model (also known as Breast Cancer Risk Assessment Tool, BCRAT)Gail 1 model estimates the absolute risk for both invasive and insitu breast cancer (ductal and lobular), derived from data of the Breast Cancer Detection Demonstration project. Gail 2 model is a modification of Gail 1, used to estimate the risk of invasive breast cancer using data from Surveillance, Epidemiology and End Result (SEER) Programme of the US National Cancer Institute.

i. Calibration performance • Validation cross-population showed underestimation of breast cancer cases in two studies,

overestimation of breast cancer cases was observed in one study37 and pool result showed it has good calibration

• PoolE/Oratiowas1.13(95%CI0.80to1.60)forGail1model(I-squared:95.0%)and0.95(95%CI 0.92 to 1.02) for Gail 2 model (I-squared: 92.5%)

• E/O ratio ranges from 0.86(95% CI 0.82 to 0.90), 0.87(95% CI 0.85 to 0.89) in white post-menopausal women aged 50 to 74 years, to E/O ratio of 0.85(95% CI 0.83 to 0.88) in Hispanic and non-Hispanic post-menopausal women aged 50 to 79 years; both in US for Gail 2 model

• E/Oratiowas1.85(95%CI1.68to2.04)amongwomenaged50to64inSingaporeforGail2model

Updating the Gail 2 model improved models calibration • E/Oratioof1.03(95%CI1.00to1.05) [using Surveillance Epidemiology End Result (SEER) breast cancer incidence and mortality

1995 to 2003)]• E/Oratioof1.00(95%CI0.94to1.08)(usingSEER2003to2006)ii. Discriminative ability • Poolconcordance(c)-statisticswas0.63forGail2model(I-squared:94.5%)


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• Overallareaundercurve(AUC)andc-statisticsrangesfrom0.54to0.63forGail2modelinthethree validation studies in the US

- [0.58 in the US white post-menopausal women aged 50 to 74 years, between 0.58 (95% CI 0.56 to 0.58) for non-Hispanic white and 0.63 (95% CI 0.58 to 0.67) for Hispanic US postmenopausal women aged 50 to 79and 0.54 (95% CI 0.52 to 0.56) among Vermont women aged 70 years and older]

Performance of Contraceptive and Reproductive Experience (CARE) modeli. Calibration performance• Validation in a single study showed underestimation of breast cancer cases with E/O ratio of

0.88 (95% CI 0.82 to 0.94) among black women aged 30 to 69 yearsii. Discriminative ability• Age-specific c-statistics for total invasive breast cancer was 0.57 (95% CI 0.55 to 0.59)• Age-adjusted c-statistics for specific breast cancer subtypes: 0.59 (95% CI 0.57 to 0.61)(Estrogen receptor positive (ER+) breast cancer) 0.54 (95% CI 0.50 to 0.57)(Estrogen receptor negative (ER-) breast cancer)

Performance of model by Petracci i. Calibration performance• Validation in a single study showed the model was well calibrated with E/O ratio of 1.10 (95% CI

0.96 to 1.26) among Italian women aged 35 to 64 yearsii. Discriminative ability • c-statistics was 0.62 (95% CI 0.55 to 0.69)(women younger than 50 years)• c-statistics was 0.57(95% CI 0.52 to 0.61)(women more than 50 years)

Performance of model by Pfeiffer i. Calibration performance• Validation in a single study showed the model was well calibrated with E/O ratio of 1.00 (95% CI:

0. 96 to1.04) among white women aged 30 to 55 yearsii. Discriminative ability • Overall AUC was 0.58 (95% CI 0.57 to 0.59) in the above validated population

Performance of Vermont modeli. Discriminative ability Validation in a single study showed the model discriminative power was modest with c-statistics

of 0.55 (95% CI 0.53 to 0.58) among Vermont women aged 70 years and older

Performance of model by Anothaisintaweei. Calibration performance• Validation in a single study showed the derived model was well calibrated with O/E ratio of 1.00

(95% CI 0.82 to 1.21) ii. Discriminative ability • C-statistics (overall) was 0.65 (95% CI 0.59 to 0.70) for prediction of breast cancer cases

Performance of BWHS modeli. Calibration performance• Validation in a single study showed the BWHS model was well calibrated with E/O ratio of 0.96

(95% CI 0.88 to 1.05) ii. Discriminative ability • AUC (overall) was 0.59 (95% CI 0.56 to 0.61)

Safety• The only reported adverse event was anxiety Women who had higher risk status had an odd of

having increased anxiety about 5 times greater than women who had lower risk status (OR 5.03; 95% CI 1.54 to 16.43)

• About 18.9% of subjects disagree with tailored risk associated with BCRAT (Gail model)

Cost implication• No evidence retrieved on breast cancer risk assessment/prediction model


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• Potential direct cost implicated on the designing, developing, testing and commissioning of available one breast cancer risk prediction model into a health risk assessment module was given at approximately RM75,000

Organizational Computerized risk estimate using any model requires computer literate user/patient and internet access.

Cancer risk prediction model needs to be continually calibrated and revalidated. The complexity of prediction modelling research from developing and internally validating a prediction model, testing, adjusting or updating the model for other individuals (external validation); and assessing its impact on therapeutic management and patient outcomes need a dedicated research expertise. Uncertainties associated with risk estimates should be addressed and informed particularly when clinical decision has serious consequences.

Among ethical issues that arose following cancer risk assessment was psychological harms resulting from being labelled ‘at risk’, and additional diagnostic procedures that can artificially increase sense of risk. The ‘at risk’ label also has implication for future health care cost. Theoretically increased psychological distress from risk labelling may contribute to other healthcare demands and raising the health care cost.

ConclusionThere was sufficient good level of retrievable evidence for breast cancer risk prediction model. There were six models identified for predicting breast cancer risk with Gail model is the most widely studied and validated model in various population. The Gail model appeared to have good calibration in validation studies done cross-population; however there is considerable heterogeneity across studies. This model showed moderate performance in terms of discriminatory ability.

For other risk prediction models, there was insufficient good level of retrievable evidence with only one study each of those other models (CARE model, model by Petracci, model by Pfeiffer, Vermont model, model by Anothaisintawee and BWHS model). The models were well calibrated in the validated population however appeared modest in discriminating woman who will be having breast cancer, than for those who will not in the study population.

There was insufficient evidence on the safety related to cancer risk prediction models for the detection of women who will develop breast cancer. Although there was minor adverse psychological sequale reported among high risk women who demonstrated to be five times more likely to have increased anxiety, it may be considered relatively safe.

There was no retrievable evidence on economic evaluation of health risk assessment or risk prediction model for breast cancer, or cost implication involved in developing a new risk prediction model without genetic component for breast cancer retrieved. The cost involved in validating a model by a prospective cohort validation study could be very costly depending on the number of study participants and years of follow up. However potential direct cost implicated to the designing, developing, testing and commissioning of available one breast cancer prediction model was given approximately at RM75,000.

Cancer risk prediction models need continual validation to give meaningful risk estimate and to ensure its applicability in the setting it will be used. The complexity to develop and subsequently validate any breast cancer risk prediction model specifically models without genetic component is reflected in the necessary local data required and availability of dedicated research expertise to create a robust model with consistent performance.

RecommendationAlthough the above review showed that the Gail model had good calibration and moderate discriminative ability, it is not suitable to be introduced as one of the strategy in the prevention of breast cancer under the Malaysian National Cancer Control Programme yet as it needs further validation to develop a well-fitted model that would have better predictive ability tailored to Malaysian population. In addition, this model needs continual validation to determine the consistency of its performance.


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TABLE OF CONTENTS

Disclaimer i

Authors and Information specialist ii

Expert committee ii

External reviewers iv

Acknowledgement and Disclosure iv

Executive summary iv

Abbreviations x

1 BACKGROUND 1

2 TECHNICAL FEATURES 3

3 POLICY QUESTION 7

4 OBJECTIVES 7

5 METHODS 8

6 RESULTS 9

6.1. Study description 10

6.1.1. Risk of bias 11

6.1.2. Review of Gail model 11

6.2. Effectiveness (predictive accuracy) of breast cancer risk prediction models 12

6.2.1. Gail model 12

6.2.2. CARE model 13

6.2.3 Other breast cancer risk prediction models 13

6.2.4. Comparison of breast cancer risk prediction models 27

6.3. Safety 32

6.4. Cost implication 35

6.5. Organizational 36

7 DISCUSSION 37

8 CONCLUSION 40

9 RECOMMENDATION 40

10 REFERENCES 41

APPENDICES

Appendix 1- Hierarchy of evidence for effectiveness studies 44

Appendix 2- Health Technology Assessment Protocol 45

Appendix 3- Search strategy 48

Appendix 4- CASP checklist 51

Appendix 5- Evidence Table (Included studies) 52

Appendix 6- List of excluded studies 69


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ABBREVIATIONS

ACS American Cancer Society

AUC Area under curve

ASR Age Standardized Incidence Rate

c-statistic concordance statistic

BCDPP Breast Cancer Detection Demonstration Project

BCRAT Breast Cancer Risk Assessment Tool

BWHS Black Women Health Study

BOADICEA Breast and Ovarian analysis of disease incidence and carrier estimation algorithm

CARE Contraceptive and Reproductive Experience

CEA Cost-effectiveness analysis

CUA Cost-utility analysis

CI Confidence interval

E/O expected over observed ratio

EBM Evidence Based Medicine

GM Gail model

HBOC Hereditary breast and ovarian cancer syndrome

HRA Health Risk Assessment

IBIS International Breast Cancer Intervention Study

LPPKN Lembaga Pembangunan Penduduk dan Keluarga Negara

MSS Manchester Scoring System

NCR National Cancer Registry

NHMS National Health Morbidy Survey

NHW non Hispanic white

NIH-AARP NIH Diet and Health Study

NPV negative predictive value

OCP Oral ContraceptivePill

OR Odds ratio

PLCO Prostate, Lung, Colorectal and Ovarian cancer screening trial

RCT Randomised controlled trial

ROC Receiver operating curve

RR relative risk

PPV positive predictive value

RCT randomised controlled trial

SBCSP Singapore Breast Cancer Screening Project

SD Standard deviation

SEER Surveillance Epidemiology End Result

US United States

U.S.A. United States of America

WHI Woman Health Initiative


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BREAST CANCER RISKPREDICTION MODEL FOR HEALTH RISK

ASSESSMENT MODULE

1 BACKGROUND

Breast cancer is the most commonly diagnosed cancer among women worldwide which represents 1 in 4 of all cancers and the most common cause of cancer death (522,000 deaths in 2012). World-wide, it is also the second most commonly diagnosed cancer. Globally, the incidence is rising with 1.7 million women were diagnosed with breast cancer in 2012 and mortality has increased by 14% since the 2008 estimates.1 Incidence rates is higher in more developed region, however mortality is much higher in less developed countries due to lack of early detection and access to treatment fa-cilities.1 In Malaysia, the National Cancer Registry (NCR) 2007 reported that breast cancer was the most commonly diagnosed cancer in women with overall age standardized incidence rate (ASR) of 29.1 per 100,000 population.2 Though ASR was dropped from 39.3 per 100,000 population in 2006,3 it was higher than incidence in other developing countries with ASR of 20 per 100,000 population.4 The rising incidence is worsened by the fact that about 30 to 40% of Malaysian women presented at a later stage of breast cancer (stage III and IV) compared to other counterparts in the developing countries.5 The increase has been said related to changes in dietary and reproductive pattern, ur-banization and ageing population.6,7 Hence, affordable and effective approaches in cancer control are needed for early detection, diagnosis and treatment of breast cancer particularly in the less developed countries.1

The growing public awareness of breast cancer and its risk factors with availability of med-ical and surgical risk reduction options has led consultation of many women on their breast cancer risk.8 The widely quoted general population lifetime risk of having breast cancer is one in eight to one in 12, and the risk in any given decade was never greater than one in 30. The proportion of all female deaths due to breast cancer per decade is never greater than 20% with the greatest propor-tion from the middle age group (35 to 55 years).9 For familial breast cancer, approximately five to ten percent of cases occur in women with Hereditary Breast Ovarian Cancer Syndrome (HBOC), which has mutation in BRCA1 and BRCA2 genes.10 Women with BRCA1 and BRCA2 mutation have a 50% to 80% lifetime risk of breast cancer. These women also have a 40% to 60% lifetime risk of having contralateral breast cancer.10,11 Specific BRCA mutation is clustered among certain ethnic groups such as Ashkenazi Jews, in the Netherlands, Iceland and Sweden. A woman’s breast cancer risk is an important consideration when making recommendation about screening mammography as screening is more beneficial for those at higher risk of developing the disease.12 The organized breast cancer screening programmes using mammography has been well established in Europe and Northern America resulting in improved five year survival rate, as high as 89%.13 Providing breast cancer screening programme using mammography to every woman in most developing countries is not feasible, thus identifying women with relatively higher risk of developing breast can-cer is a promising alternative.14

Considerable effort has been directed at identifying risk factors and developing risk pre-diction models for breast cancer. Multiple prediction models have been developed to assist with breast cancer risk prediction efforts.10 Prediction model of breast cancer is a mathematical equation designed to quantify the risk an individual woman would develop breast cancer in a defined peri-od.14 Accurate breast cancer risk assessment is vital to personalize screening and risk reduction strategies.8Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions, as well as to counsel those at risk and determine eligibility for prospective prevention trial.15,16 These empirically derived models are used in clinical setting to guide decision making about future screening behaviour or adoption of risk reduction strategies.17 These models


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have been used to estimate the costs of population burden of cancer, plan intervention trials, cre-ate benefit-risk indices and design prevention strategies for at risk populations.18 It is also meant to personalize management strategies for all women with the aim of increasing survival in high-risk women while decreasing cost and complications in low-risk women.19 Given the variance in breast cancer risk, surveillance and primary prevention adapted to individual risk level may be the most effective use of resource for preventing, detecting and improving breast cancer survival.17

Risk factors are often required as input variables in the prediction model.14 These risk factors need to be assessed to evaluate its risk over time including family history, hormonal and reproduc-tive risk factors such as the use of exogenous hormones (estrogen and progesterone), endogenous hormonal factors (ages at menarche, menopause and first childbirth), and environmental risks such as alcohol intake, diet, exercise, obesity, as well as increase breast density.20 The Malaysian CPG on Management of breast cancer stratify risk factor as low (relative risk RR 1.0 to 1.4), moderate (RR 1.5 to 2.0) and high (RR of more than 2.0).21 Models containing modifiable risk factors are of particular interest to patients and those involved in reducing population incidence rates15 as 21% of all breast cancer deaths worldwide are attributable to alcohol use, overweight and obesity, and physical inactivity.22

Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all level of risks. Risk may be assessed as likelihood of develop-ing breast cancer or as likelihood of detecting a BRCA1 or BRCA2 mutation.19 Types of cancer risk assessment models are; model aimed at assessing breast cancer risk over time or to predict proba-bility of being diagnosed with a cancer; and the other aimed at assessing mutation probability of an individual or carrying a gene mutation that predisposes to a particular cancer.6 The most commonly used models for the former type were the Gail and Claus model.19 Some of the latter models are the Breast and Ovarian Analysis Of Disease Incidence And Carrier Estimation Algorithm (BOADICEA), BRCAPRO, Couch, Shattuck Eiden, Frank, Tyrer-Cuzick (IBIS models) and Manchaster scoring.6, 19 Each model has unique attributes stemming from the methodology, sample size and population characteristics used to create the model. Understanding the strength and weaknesses of each model facilitates accurate breast cancer risk assessment.19 The Gail model provides five-year and lifetime risk estimate of having breast cancer and is accessible on the internet. The Claus model is useful for assessing breast cancer risk with a family history, similarly with BRCAPRO, BOADICEA and IBIS models which can produce both mutation probabilities and breast and ovarian cancer estimates.16

Identification of individual with a suspected heritable cancer syndrome (HBOC) has im-plications for evaluation and application of risk reducing options such as chemoprevention and prophylactic surgery.23 These underline the need for risk prediction model for breast cancer, not only to predict which women will develop the disease, but also to apply appropriate intervention and lifestyle measures in order to prevent the disease.6 Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such mod-els by professionals must guide their decision-making, improve patient outcomes and the cost-ef-fectiveness of care.24 Better understanding of these model performances, including the strengths and limitations is needed before applying them in clinical practice.

In Malaysia, currently there is no breast cancer risk assessment/prediction model in the prediction of individual risks of developing breast cancer in existing health risk assessment (HRA) module of Ministry of Health. An opportunistic screening policy is currently being practiced for breast cancer in Malaysia by means of both primary and secondary prevention approach as part of cancer control strategy.7 Therefore, there is a need to assess the feasibility of having a risk as-sessment/prediction model as a HRA module in enhancing early detection of breast cancer by cap-


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turing wider coverage of general population, in facilitating effective cancer control strategies in the country. This HTA was requested by a Senior Principal Assistant Director, Health Education Division, Ministry of Health.

TECHNICAL FEATURES

2.1 BREAST CANCER RISK PREDICTION MODEL

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors (e.g. age, menopausal status) will develop a future condition, such as breast cancer, within a certain time period (such as within five years or lifetime).15 It uses multiple predictors (covariates) to estimate the probability or risk that a certain outcome is present.24

The goal of risk prediction is to provide individualized risk (absolute risk) with associated measures of uncertainty thus stratify individuals by these risks.24,25Other risk parameters of interest in clinical oncology are relative risk and survival probability.The absolute risk is an important estimate in clinical setting that assist in individual decision making. 24,25

These models are used/applied in planning cancer intervention and screening trials, assisting in creating benefit-risk indices, estimating the population burden, cost of cancer and impact of specific intervention, identifying individuals at high risk who may benefit from targeted screening or chemoprevention, designing population prevention strategies and improving clinical decision making.18

Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care.24

2.2 DEVELOPING AND VALIDATING BREAST CANCER RISK PREDICTION MODEL

Risk prediction is accomplished by identifying characteristics that are associated with high or low risk of developing a disease and then combining those characteristics in a statistical model to produce probability estimate of developing the disease over a given period. 26

Risk models combine the baseline risk of developing the condition with an individual’s risk score, i.e. a score derived from their set of risk factor values multiplied by the ‘beta’ weights (e.g. log odds ratios) associated with these factors, as estimated from a statistical equation. The baseline risk of the condition represents the underlying population risk for patients whose risk factor values are all zero (or ‘not present’), and this is usually estimated from a prospective population-based cohort study. The risk-score component shows how much the baseline risk is multiplied for increasing values of the risk factors, and may also be estimated using a cohort study or, for rare conditions, a case-control study. 15

The two main statistical models used to identify important risk factors and to estimate their associated beta weights are logistic regression and Cox proportional hazards regression. 15

The variables in the model can be any combination of environmental, behavioural, genetic or psychological attributes of the person. As well as estimating risk estimates for specific individuals, risk predictions model can also make a population-based estimate of risk by using average risk factor values from the population. 15


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Initial effort at defining breast cancer risk were primarily empirical.Later estimates of breast cancer or probability of being BRCA1/2 carrier were derived using statistical techniques such as logistic regression, Bayesian analyses, genetic modelling using the method of maximum likelihood and log-incidence models.25

After a risk prediction model has been developed in a sample from a population, it then needs to be validated in further independent samples from the same population, and indeed within samples from different populations to ensure that it is reliable and generalisable. Frequently the predictive accuracy of the model is not as good in the validation sample as the original sample, and so adjustments are made leading to new or modified models being gradually developed over time. Even if the same risk factor variables are included, their beta weights may be changed which would then constitute a different model, as the risk score would then change. The performance of prediction models may also vary according to the population they are applied to, so that a model may have good accuracy in a high risk population and not in a low risk population and vice versa.15

Model validation is taking the original model or simplified score with its predictors and assigned weights (eg regression coefficient) as estimated from the development study, measuring the predictor and outcome values in the new individuals, applying the original model into these data and quantifying the model’s predictive performance. Developed models need to provide accurate and validated (internally and externally; temporal, geographical and domain/setting) estimates of disease probabilities. 24

2.3 PERFORMANCE OF CANCER RISK PREDICTION MODEL

Variety of ways is available to describe the performance of predictive tests. Many of the statistical measures used are well-known from diagnostic test studies, such as sensitivity, specificity and the AUC (area under the receiver operating characteristic curve (ROC)). In practice, only some of these statistics are reported in modelling and validation articles for risk prediction models. The two most common are the expected over observed ratio (E/O) statistic and the concordance (c) statistic, interpreted as follows:

• TheE/Ostatisticmeasuresthecalibrationperformanceof themodel.Calibrationorreliabilityassesses the ability of a model to predict the number of events in subgroups of the population.18

Calibration is most commonly evaluated by use of goodness of fit or chi-square statistic which compares the observed number (O) and expected numbers of events (E), so a well fitting model should have the number close to 1. A number lower underestimates the incidence of the condition whereas a number higher overestimates the incidence. Often E/O statistics are presented for deciles of the population defined by predicted risk, to see whether E/O is close to 1 in all deciles or not.15 Good calibration is important in all models, particularly in those used to estimate population disease burden and to plan population level intervention. Recalibration of a model can be performed when risk is systematically over or underestimated. 18

• The concordance (c) statistic which corresponds to the area under the receiver operatingcharacteristic curve measures the discrimination performance of the model. 15,18 Discrimination measures a model’s ability to distinguish at the individual level between those who will develop the disease and those who will not. It gives the proportion of randomly chosen pairs from the sample (i.e. a person with the condition paired with one without it), where the person with the condition has a higher predicted risk than the one without. AUC statistic of 0.5 is equivalent to no discrimination between people who develop the condition and those who do not, whereas 1.0 indicates perfect discrimination.15 Each point on the ROC curve shows the effect of a rule for turning of a risk estimate into a prediction of the development of breast cancer in woman. Good discrimination in a model is important for decisions made at individual level (clinical decision making and screening).18

Performance assessment of the models is commonly assessed in two ways; at the population level and at the level of individual woman. Model performance at population level is assessed by comparing the number of women in their study who the model estimated (E) would develop breast


5

cancer with the number of women who actually were diagnosed with breast cancer (O) resulting in the overall E/O ratio. 26 Performance of the model could also be assessed at the individual level. At this level, a model that discriminates well should consistently predict a higher risk of breast cancer for woman who will be diagnosed with the disease, than for women who will not. Concordance statistic value ranges from 0.50 (equivalent to coin toss) to 1.0 (perfect discrimination).26

Accuracy scores including positive and negative predictive values can be used to evaluate how well a model categorizes specific individuals. This measure is especially helpful in evaluating models used for clinical decision making. However, even with good sensitivity and specificity, the positive predictive value may be low especially for rare disease.18

2.4 TYPES OF MODEL RELATED TO BREAST CANCER PREDICTION

2.4.1 Modeltodefinetheriskofdevelopingbreastcancer

Many risk prediction models have been developed that have looked at a variety of different risk factors for developing breast cancer.The number of models has grown steadily, with the most well-known is the Gail model and other available models are such as the Claus model, the Tyrer-Cuzick model and the Jonker model. 15

Among the published breast cancer risk prediction models (absolute risk prediction) are Ottman et al (1983), Anderson et al (1985), Gail et al (1989), Taplin et al (1993), Claus et al (1993), Rosner et al (1996), Colditz et al (2000), Ueda et al (2003) and Tyrer et al (2004).18

2.4.1.1 Gail model

Gail and colleagues (1989) described a risk assessment model that focuses primarily on nongenetic risk factors with limited information on family history. This model is used to estimate a woman’s risk of developing invasive breast cancer over specific periods of time. Health care providers may use this model to assess breast cancer risk, inform decision-making about chemoprevention strategies, or to help determine eligibility for clinical trials.27

The model was originally designed to determine eligibility for the Breast Cancer Prevention Trial and has been made available in the National Cancer Institute website. Individualised breast cancer probabilities from information on relative risk and baseline hazard rate are generated.6

The Gail model considers the following factors to assess breast cancer risk:- Personal medical history, including the number of previous breast biopsies and the presence of

atypical hyperplasia (a precancerous condition) in a previous biopsy- Reproductive history (age at the start of menstruation and age at the first live birth of a child)- Family history of breast cancer among first-degree relatives

Limitation:- The model considers limited information about family breast cancer history, which could

underestimate risk - It does not include several breast cancer-specific risk factors, such as personal history of

breast cancer, use of hormone replacement therapy, breast density, breast feeding, or age at menopause

- It does not include lifestyle factors that may increase risk, such as smoking, alcohol consumption, diet or physical inactivity


6

2.4.1.2 Breast Cancer Risk Assessment Tool (BCRAT)

The Gail model 1 is publicly available in a slightly modified version as the Breast Cancer Risk Assessment Tool (http://www.cancer.gov/bcrisktool)27. The Breast Cancer Risk Assessment Tool is based on the Gail model. This tool has been tested (validated) using data from large studies of women and been shown to provide accurate estimates of breast cancer risks in white women as well as African American and Asian and Pacific Islander women in the United States.

This online tool estimates a woman’s risk of developing invasive breast cancer over a 5-year time period and over her lifetime.

The tool estimates breast cancer risk based on the following risk factors: - Age- Age at first period- Age at first live birth of a child- Family history of breast cancer- Personal history of breast cancer- Number of past breast biopsies, including number of breast biopsies that showed atypical

hyperplasia (a precancerous condition that increases breast cancer risk)- Race/ethnicity

Its score provides an estimate of the average risk for a group of women with similar risk factors. For example, if the tool gives a score of 1.7 per cent, this means that an estimated 1.7 per cent of women with similar risk factors will develop breast cancer in the next 5 years. Limitations of this model are similar to those of Gail model.

2.4.2 Modeltodefinetheriskofbeingacarrierofmutationinbreastcancersusceptibilitygene

Among the published breast cancer risk prediction models (for gene carrier status) or genetic risk models are Couch et al (1997),Shattuck-eidens et al (1997), Parmigiani et al (1998), Berry et al (1998), Frank et al (1998), Antoniou et al (2002), de la Hoya et al (2002), Vahteristo et al (2001), Apicella et al (2003), Jonker et al (2003) and others.18

Some of the widely used mutation carrier prediction algorithms are Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), BRCAPRO, IBIS and eCLAUS model. These models assume an underlying genetic model for breast cancer susceptibility.28

2.4.2.1 Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA)

The BOADICEA is a computer program (risk prediction model) used to calculate the risks of breast and ovarian cancer in women based on their family history. It is also used to compute BRCA1 and BRCA2 mutation carrier probabilities and age specific risk of breast and ovarian cancer. It also estimates the likelihood of a woman developing breast cancer in five years, and over the course of her lifetime. The tool is used to help inform a woman’s decision-making about genetic counselling and testing. The programme is free for anyone to use and risk can be calculated using the BOADICEA Web application. It was developed using complex segregation analysis of breast and ovarian cancer based on a combination of families identified through population based studies of breast cancer and families with multiple affected individuals who had been screened for BRCA1/2 mutations.27

The tool estimates breast cancer risk based on:- Family cancer history- Genetic mutation status


7

Results: A decision to go for genetic counselling is usually made when the model predicts a 10% or greater chance that the patient has a mutation of the BRCA1, BRCA2, or both genes. Limitation:- The major limitation of this tool is that it only considers genetic mutation status and family history.

It does not take any other breast cancer risk factors into account. - The tool was developed using data for the United Kingdom. It may be applicable to populations in

other high-income countries; however estimations of risk in other populations may be unreliable.

2.4.2.2 IBIS Breast Cancer Risk Evaluation Tool

The IBIS tool (also called the Tyrer-Cuzick model) is used to calculate a person’s likelihood of carrying the BRCA 1 or 2 mutations, which are associated with increased breast cancer risk. It estimates the likelihood of a woman developing breast cancer in 10 years and over the course of her lifetime. The tool is used to help inform a person’s decision-making about genetic counselling and testing. 29

The tool estimates breast cancer risk based on the following risk factors: - Age- Age at first live birth of a child- Age at menopause- Height and weight- Use of hormone replacement therapy- Comprehensive family history

Result: Genetic counselling is advised when the model predicts a 10% or greater chance that the person has a mutation of the BRCA1, BRCA2, or both genes. The tool does not include risk factors associated with lifestyle or breast density as its limitation.29 Major advantage over BRCAPRO is that this model allows for the presence of multiple genes of differing penetrance. It does produce a readout of BRCA1/2, but also allows for a lower penetrance of BRCAX.6

2.4.2.3 BRCAPRO model

This is a Bayesian model developed by Parmigani and colleagues that incorporated published BRCA1 and BRCA2 mutation frequencies, cancer penetrance in mutation carriers, cancer status (affected, unaffected or unknown) and age of consultees’ first degree and second degree relatives. An advantage of this model is that it includes information on both affected and unaffected relatives. It also provides estimates for the likdlihood of findingeither BRCA1 or BRCA2 mutation in the family. An output that calculates breast cancer risk using the likelihood of BRCA1/2 can be utilised. Major drawback from breast cancer risk assessment aspect is that no other ‘genetic’ element is allowed for. As such, this model will underestimate risk in breast-cancer-only families.6

3 POLICY QUESTION

i. In Ministry of Health, should a breast cancer risk prediction model for health risk assessment (HRA) module be introduced as one of the strategies in the prevention of breast cancer under the Malaysia National Cancer Control Programme?

ii. If breast cancer risk prediction model for HRA module is to be introduced, which risk prediction model should be adopted in Malaysia?

4 OBJECTIVES

i. To assess the effectiveness in term of predictive accuracy of breast cancer risk assessment/prediction models among women

ii. To assess the safety, organizational, ethical issues and economic implications related to risk assessment/prediction models for breast cancer among women


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4.1 RESEARCH QUESTIONS

i. What are the effectiveness in term of predictive accuracy of breast cancer risk assessment/prediction models among women

ii. What are the safety, organizational, ethical issues and economic implications related to the use of breast cancer risk assessment/prediction models among women

5 METHODS

5.1 LITERATURE SEARCH STRATEGY

Studies were identified by searching electronic databases. The following databases were searched through the Ovid interface: MEDLINE(R) In-process and other Non-Indexed Citations and Ovid MEDLINE(R) 1946 to present. EBM Reviews-Cochrane Database of Systematic Reviews (2005 to May 2015), EBM Reviews-Cochrane Central Register of Controlled Trials (May 2014), EBM Reviews – Database of Abstracts of Review of Effects (2nd Quarter 2015), EBM Reviews-Health Technology Assessment (2nd Quarter 2015), EBM Reviews-NHS Economic Evaluation Database (2nd Quarter 2015). Parallel searches were run in PubMed. Appendix 3 showed the detailed search strategies. No limits were applied to the search. The last search was run on 20 March 2015. Additional articles were identified from reviewing the references of retrieved articles.

5.2. STUDY SELECTION Based on the policy question the following inclusion and exclusion criteria were used:-

5.3. INCLUSION CRITERIA

• Population : Adultwomanmorethan18yearsold• Intervention : Breastcancerriskassessment/riskpredictionmodel/HealthRisk

Assessment models involving single or multiple risk factors• Comparators :-• Outcome : i. Performance of available breast cancer risk prediction models

in terms of its predictive accuracy - sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), calibration as measured by expected/observed ratio, discrimination as measured by AUC or c-statistic

ii. Strengths and weaknesses of retrieved risk prediction model (qualitative)

iii. Effectiveness/benefit of the breast cancer risk assessment/predictive model related to patient outcome as measured by detection rate, acceptance, awareness, receptiveness

iv. Organizational (operational, training, resources), ethical, legal and economic implication

• Study design : No restriction of study type. HTA report, Systematic Review, Randomised Controlled Trial (RCT), Non Randomised Controlled Trial, diagnostic accuracy studies, Observational Studies (Cohort study, Case Control study, Cross Sectional study with gold standard), and economic evaluation studies such as cost-effectiveness / cost-utility analysis.

• Full text articles published in English.


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5.4 EXCLUSION CRITERIA:-

• Study design: Animal study, laboratory study, narrative review, case reports. • Studies on risk assessment/ risk prediction/ health risk assessment models for breast cancer

with any genetic component as risk factor • Studies in women who already had breast cancer when recruited or studies involving male subjects• Non English full text article.

Based on the above inclusion and exclusion criteria, study selection were carried out independently by two reviewers. All identified citations (titles and abstracts) were assessed for the above eligibility criteria. If it was absolutely clear from the title and / or abstract that the study was not relevant, it was excluded. If it was unclear from the title and / or the abstract, the full text article was retrieved. Full text articles were ordered for all included and possibly included citations. Two reviewers assessed the content of the full text articles and did data extraction. Disagreements were resolved by discussion.

5.5. QUALITY ASSESSMENT STRATEGY The methodological quality of all the relevant full text articles retrieved was assessed using the Critical Appraisal Skills Programme (CASP) tool by two reviewers.30 For SR the criteria assessed include selection of studies, assessment of quality of included studies, heterogeneity of included studies. For cohort study, the criteria assessed were selection of the cohort, accurate measurement of exposure and outcome, confounding factors, follow-up adequacy and length. The CASP checklist is as in Appendix 4. All full text articles were graded based on guidelines from the U.S./Canadian Preventive Services Task Force (Appendix 1).31

5.6 DATA EXTRACTION STRATEGY

Data were extracted from the included studies by a reviewer using a pre-designed data extraction form (evidence table as shown in Appendix 6) and checked by another reviewer. Disagreements were resolved by discussion. Details on : (1) methods including study design, (2) study population (3) type of intervention, (4) comparators, (5) outcome measures including economic evaluation and organizational issues were extracted. Other information on author, journal and publication year, and study objectives were also extracted. The method to assess the model performance (predictive ability) which included calibration using a ratio of expected over observed value (O/E) with 95% confidence interval or goodness of fit test and discrimination using ROC analysis or concordance statistic were recorded. If studies had validated the prediction models, the type of validations, method used, and result of validation were also recorded. Modification of the previous prediction model by the author was recorded, either by removing or modifying standard variable or adding new variable. The extracted data were presented and discussed with the expert committee.

5.7 METHODS OF DATA SYNTHESIS Data on the effectiveness (predictive accuracy), related safety issues, organizational issues and cost implication of risk assessment/ risk prediction/ health risk assessment models for breast cancer were presented in tabulated format with narrative summaries. No meta-analysis was conducted for this review.

6 RESULTS

A total of 805 titles were identified through the Ovid interface: MEDLINE(R) In-process and other Non-Indexed Citations and Ovid MEDLINE(R) 1948 to present, EBM Reviews-Cochrane Database of Systematic Reviews (2005 to March 2015), EBM Reviews-Cochrane Central Register of Controlled Trials (July 2014), EBM Reviews – Database of Abstracts of Review of Effects (1st Quarter 2015), EBM Reviews-Health Technology Assessment (1st Quarter 2015), EBM Reviews-NHS Economic Evaluation Database (1st Quarter 2015), and PubMed. Twenty-five were identified from references of retrieved articles.

After removal of 170 duplicates, 626 titles were screened. A total of 181 titles were found to be potentially relevant and 123 abstracts were screened using the inclusion and exclusion criteria. Of these, 65 abstracts were found to be irrelevant. Fifty-eight potentially relevant abstracts were retrieved in full text.


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After applying the inclusion and exclusion criteria and critical appraisal to the 65 full text articles, 14 full text articles were included and 55 full text articles were excluded. Twelve (12) out of the fourteen included articles were related to effectiveness in terms of predictive accuracy of breast cancer risk prediction models whereas the other two (2) were related to safety aspect of the models. The fourteen (14) full text articles finally selected for this review were comprised of two systematic reviews, one randomised trial and eleven observational studies (eight cohorts and three cross sectional). No evidence on cost-effectiveness / cost-utility analysis was retrieved.

Forty-five articles were excluded due to irrelevant study design and including those primary studies already included in the systematic review (n = 10), irrelevant population (n = 1) irrelevant intervention (n = 32), irrelevant outcome (n = 2) and). The excluded articles were listed as in Appendix 7. There was no HTA report on risk assessment/risk prediction/ health risk assessment models for breast cancer.

Figure 1: Flow chart of study selection

Number of titles identified via Ovid (n=595) and PubMed (n=176), Total (n=771)

Total number of titles identified (n=796)

Titles screened (n=626)

Relevant titles (n= 181)

Abstracts screened (n=123)

Potentially relevant abstracts retrieved in full text (n=58) (manuscripts review and

application of inclusion and exclusion criteria)

Full text included (n=14)

Number of titles identified from references (n=25)

Duplicates (n=170)

Titles not relevant (n=445)

Editorial/letters/no abstracts (n=58)

Abstracts not relevant (n= 65)

Full text excluded (n=44)Study design not relevant n=10) Irrelevant population (n=1) Irrelevant intervention (n=31)Irrelevant outcome (2)

6.1 STUDY DESCRIPTION

Fourteen (14) full text articles were finally selected for this review which comprised of two systematic reviews, one randomised controlled trial and eleven observational studies (eight cohorts and three cross sectional).


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Of the 14 articles, twelve studies were eligible and met the inclusion criteria thus included in the effectiveness (predictive accuracy) of breast cancer risk prediction models in this review. They were two systematic reviews and ten observational studies (eight cohort studies and two cross sectional studies). The other two (2) articles were studies related to safety aspect of the models.

The included articles were published between 2009 and 2015. Both the included systematic reviews were published in 2012, in which their search were done up to June and October 2010. Most of the observational studies were conducted in the U.S.A., with one each from Italy, Singapore, Thailand and Korea. The total pooled sample size of all included studies was 437,049 subjects. Samples sizes for each of the observational studies ranged from 690 to 135,329 subjects. The length of follow-up ranged from five years to ten years.

Most of the study participants were general women without history of breast cancer at premenopausal and postmenopausal ages from white, African-American and Hispanic ethnicity. One study assessed the model performance in Asian women and one specific for Thai women. Most of the studied population were from various large cohort studies such as Black Women’s Health Study, NIH Diet & Health Study (NIH-AARP), US Prostate, Lung, Colorectal and Ovarian Screening Trial (PLCO) study; cohort of women in Vermont Breast Cancer Surveillance System underwent breast cancer screening programme, Woman Health Initiative (WHI) study, Italian Florence-EPIC cohort and Singapore Breast Cancer Screening Project.

6.1.1 Risk of bias

Risk of bias in the included cohort studies are summarised as in Table 1.

Table 1: Assessment of risk of bias in included cohort studies

Criteria/Studies

Selection (cohort)

recruited in acceptable

way

Exposure accurately measured

Outcome accurately measured

Confounding factors taken into account

Follow-up complete and long enough

Schonfeld 2010 33 Yes Yes Yes Can’t tell Yes

Chay 2012 37 Yes Yes Yes Can’t tell Yes

Banegas 2012 36 Yes Yes Yes Can’t tell Yes

Boggs 2013 38 Yes Yes Yes Can’t tell Yes

Petracci 2010 40 Yes Yes Yes Can’t tell Yes

Pfeiffer 2013 41 Yes Yes Yes Yes Yes

Boggs 2015 42 Yes Yes Yes Can’t tell Yes

Vacek 2011 12 Yes Can’t tell Yes Can’t tell Yes

Cross sectional study have potentially higher risk of bias.

6.1.2 Review of Gail Model

Gail model was one of the earliest model, developed in 1989 and described here as Gail.32 It was developed through a case - control study of women participated in the American Cancer Society’s mammography feasibility study, the Breast Cancer Detection and Demonstration Project. It estimates the risk an American woman would develop invasive breast cancer over specified time interval e.g., five or ten years given age and risk factors. According to Schonfeld,33 the probability of developing breast cancer in the Gail 1 model is computed by combining relative risks (RRs) estimated from the Breast Cancer Detection Demonstration Project (BCDDP) with attributable risks, age-specific breast cancer incidence rates, and competing mortality rates from all other causes from the Surveillance, Epidemiology and End Results (SEER) program for the years 1983 to 1987.33

The six risk factors included in the model were age, age at first live birth, age at menarche, history of breast cancer in first-degree relatives, number of previous breast biopsies and history of atypical


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hyperplasia.34 The Gail model components of age at menarche, first degree family history of breast cancer, biopsy history, and reproductive history, emerged as having the strongest collective risk assessment ‘‘fit’’ after being screened alongside several other candidate risk factors.

Expression of these four risk factors yields a combined relative risk for the individual patient, and this relative risk is in turn converted into an ‘‘absolute’’ risk by a ‘‘baseline’’ risk multiplier. The baseline risk is computed by using Surveillance, Epidemiology, and End Results (SEER) program data on age-specific breast cancer incidence rates in White American women. 35

In 1992, Anderson published Gail 2 model in a technical report which is described by Constantino et al in 2010. 34 This model is for predicting invasive breast cancer only, and the baseline risk is estimated using SEER database available from the placebo arm of a RCT on adjuvant tamoxifen treatment. The Gail 2 model was used to establish eligibility for tamoxifen treatment by estimating baseline risk of breast cancer.34 The major limitation of Gail model is inclusion of only first degree relative, which result in underestimating risk in the 50% of families with cancer in the paternal lineage and also takes no account of the age of onset of breast cancer.6

6.2 EFFECTIVENESS (PREDICTIVE ACCURACY)

6.2.1 Gail model

Three studies reported validation of Gail (also known as BCRAT) model (Table 1). The validation population was mostly from United States (US) post-menopausal white women aged 50 to 79 years Hispanics and non-Hispanic 33,36 with one study from Asia also in women aged 50 to 64 years.37

Two studies recalibrated/updated the Gail model with SEER incidence and mortality of 1995-2003 33 and RR from SEER 1993-1997 breast cancer incidence.36

Performance statistics were reported by the three studies. Two studies found underestimation of breast cancer cases by 13 to 18% using the Gail model, however upgrading of the model demonstrated it was improved to E/O of 1.03 and O/E of 1.08 respectively33,36. Another study found the Gail model overestimated breast cancer risk among Singaporean women aged 50 to 64.37 Overall discriminative accuracy of the studies was modest with c-statistic ranges between 0.58 to 0.67.

The three articles conducting validation and recalibration of the Gail model is described here with summary of its finding is illustrated as in Table 2.

Schonfeld SJ et al conducted a prospective cohort study in the United States in 2010 to evaluate the Gail model calibration in two cohorts (National Institutes of Health AARP Diet and Health Study (NIH-AARP, 1995 to 2003), and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO, 1993 to 2006), following changes in US breast cancer incidence during the 1990s. The Gail model uses breast cancer incidence rates and competing mortality rates from the Surveillance, Epidemiology and End Results (SEER) program (1983 to 1987), while this updated model used SEER incidence and mortality rates from the period corresponding to the cohorts, 1995 to 2003. In this study, cancer ascertainment was done through linkage with state cancer registries (NIH-AARP), through annual mailed questionnaires to participants and medical records confirmation (PLCO). Reports from physicians, Death Index and cancer registries were periodically searched when available. Risk factors included in the Gail model were obtained from baseline questionnaires. Calibration of updated model was evaluated using combined Gail model RR with SEER breast cancer incidence rates 1995 to 2003. This study involved Women from NIH-AARP (a large prospective study of 200,000 women age 50 to 71 years, from 1995 to 1996) and PLCO (a multicenter screening trial involving 77,500 women, between 1993 and 2001; aged 55 to 74 years at baseline included white, post-menopausal women with known parity, no history of in-situ or invasive breast cancer, and age younger than 90 years at the start of follow-up. This study has validated the Gail model (SEER 1983 to 1987) in two cohorts. They found the Gail model significantly underpredicted the number of invasive breast cancers in both cohorts; with E/O ratio of 0.87 (95% CI, 0.85 to 0.89) in NIH-AARP (underprediction by 13%) and E/O ratio of 0.86 (95% CI, 0.82 to 0.90) in PLCO (underprediction by 14%). Updating the Gail model (using SEER 1995 to 2003), has improved its calibration in both cohorts, E/O ratio of 1.03 (95% CI, 1.00 to 1.05) in NIH-AARP and E/O ratio of 1.01 (95% CI: 0.97 to


13

1.06) in PLCO. Further updating of the Gail model (using SEER 2003 to 2006) demonstrated it was well calibrated in PLCO cohort (E/O ratio of 1.00; 95%CI, 0.94 to 1.08). The discriminatory ability was unchanged by the recalibration; with the overall AUCs for the updated model were 0.58 in NIH-AARP and 0.59 in PLCO.33

Chay WY et al had conducted a prospective cohort validation study in 2012 to evaluate the performance of the Gail Model (GM) as an appropriate breast cancer risk assessment tool in the Asian population. The study population consisted of 28,104 women aged 50 to 64 years who participated in the Singapore Breast Cancer Screening Project (SBSCP), a population-based mammography screening project among female Singaporeans conducted between October 1994 and February 1997 and did not have breast cancer during screening. Women with breast cancer or other cancers, pregnant or had mammography or breast biopsy 12 months prior study were excluded. Cancer ascertainment was done through the Singapore Cancer Registry and the National Death Register. To evaluate the performance of the GM, the expected number of invasive breast cancer cases were compared to number of actual cases observed within 5-year and 10-year follow-up. They found 144 and 409 incidence of invasive breast cancers within 5 years and 10 years from screening, respectively. In term of model calibration, The GM over-estimated the overall breast cancer risk at 5 year from screening with E/O ratio of 2.51 (95%CI 2.14 to 2.96); predicted invasive breast cancer cases were 362 cases and observed invasive breast cancer cases were 144 cases.The model was also over-estimated breast cancer risk across all age groups, with the highest discrepancy demonstrated among older women aged 60 to 64 years with E/O ratio of 3.53 (95%CI 2.57 to 4.85). Similarly at 10 year from screening the model also over-estimated overall breast cancer risk with E/O ratio of 1.85 (95%CI 1.68 to 2.04). The corresponding predicted and observed invasive breast cancer cases were 758 and 409 cases respectively. Over-prediction of the number of breast cancers by GM was higher for women who were 60 to 64 years old (E/O ratio of 2.54, 95% CI 2.57 to 4.85). Trends of E/O ratio by age group and predicted risk quintile group which were based on 10-year prediction were broadly similar to those based on 5-year prediction. The author concluded that this study validates the Gail model risk factors individually but it over-predicts the risk of invasive breast cancer in the setting of a developed Asian country, with the largest difference seen in older women aged between 60 and 64 years old. Consequently, it has become imperative to evaluate local breast cancer epidemiology in order to validate existing models of breast cancer risk. Future work should focus on the development of appropriately calibrated models for better prediction of risk, which would benefit individual counselling and cancer prevention research.37

Banegas et al in 2012 similarly carried out a cohort validation study to examine the performance of Breast Cancer Risk Assessment Tool (BCRAT) and updated BRCAT in Hispanic women in the United States. BCRAT combines 1990 to 1996 breast cancer incidences for Hispanic women with relative risk of risk factors from non Hispanic white (NHW). In this study, the relative risks, calibration and discriminatory accuracy of BCRAT risk projections for 6,353 Hispanic were compared to 128,976 NHW postmenopausal participants aged 50 to 79 in the Women’s Health Initiative study, a large longitudinal study involving Hispanic and NHW. BCRAT risk factors were obtained from the enrollment questionnaire. All reported invasive breast cancers were adjudicated locally and centrally. Calibration for an ‘updated BCRAT’ that combined BCRAT relative risks with 1993 to 2007 SEER breast cancer incidences that are simultaneous with the WHI was re-evaluated. 36

Calibration of BCRAT found that the model underestimated the number of breast cancer diagnoses by 18% among Hispanics (O/E of 1.18, 95%CI 0.99 to 1.40; p= 0.06) and by 18% as well for NHW women (O/E of 1.18, 95%CI 1.14 to 1.21; p< 0.001). The discriminatory ability of BRCAT was modest with c-statistic (AUC) of 0.63 (95%CI 0.58 to 0.67) for Hispanic women and 0.58 (95%CI: 0.56 to 0.58) for NHW women in the WHI. Validation of updated model however demonstrated an improved calibration for both Hispanic women (O/E of 1.08, p= 0.4) and NHW women (O/E of 0.98, p= 0.2). 36

6.2.2 Contraceptive and Reproductive Experience (CARE) model

We identified two studies reported on Contraceptive and Reproductive Experience (CARE) model in which one study did validation38 and another study did comparison of CARE model.39 (Table 3) The validation population was African-American women aged range from 30 to 69 years.


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Table 2: Description summary of risk prediction study characteristics related to performance of Gail model

Study author Design Model Risk factors Validated populationValidation statistic

Calibration Discriminatory ability

Schonfeld 2010 33 Cohort Gail model (SEER incidence and mortality 1983 to 1987)

& updated Gail model(SEER incidence and mortality 1995- 2003)

• Ageatmenarche• No.offirstdegree

relativewithbreastca

• Ageatfirstbirth• No.ofbreast

biopsy

White,post-menopausalwomenaged50to74yearsatbaselinewithknownparity,nohistoryofin-situorinvasivebreastcancer,andageyoungerthan90yearsatthestartoffollow-up

Fromtwocohorts;NIH-AARP(alargeprospectivestudyof200,000 women from 1995 to1996)andPLCO(amulticenterscreeningtrialinvolving77,500women,between 1993 and 2001)

i.TheGailmodel:-E/Oratio=0.87(95%CI

0.85to0.89)inNIH-AARP(underpredictionby13%),

-E/Oratio=0.86(95%CI0.82to0.90)inPLCO(underpredictionby14%),

ii.Updatedmodel(SEER1995-2003): E/Oratio=1.03(95%CI

1.00to1.05)inNIH-AARP E/Oratio=1.01(95%CI:

0.97to1.06)inPLCO.

i. UpdatedGailmodelinPLCO(SEER2003to2006):

E/Oratio=1.00;(95%CI0.94to1.08).

-

• Updatedmodel: OverallAUCswere

0.58 in NIH-AARPand0.59inPLCO.

Chay 2012 37 Cohort Gail model • Ageatmenarche• Ageatfirst

livebirth• Previousbreast

biopsy• No.offirstdegree

relativewithbreastca

28,104womenaged50to64yearsfromSingaporeBreastCancerScreeningProject(SBCSP),

apopulation-basedmammographyscreeningprojectamongfemaleSingaporeansconductedbetweenOctober1994andFebruary1997.

Excluded:womenwithbreastcancerorothercancers,pregnantorhadmammographyorbreastbiopsy12monthspriorstudy

Gail Model i.5yearfromscreening-Overall:E/Oratio=2.51(95%CI=

2.14to2.96) TheGMover-estimated

breastcancerriskacrossallagegroups,withthehighestdiscrepancyamongolderwomenaged60-64years(E/O=3.53,95%CI=2.57-4.85)

ii.10yearfromscreeningE/Oratio=1.85(95%CI=1.68to2.04)

Over-predictionofthenumberofbreastcancersbyGMwashigherforwomenwhowere60to64yearsold(E/Oratio=2.54,95%CI=2.57to4.85

-

Banegas201236 Cohort BreastCancerRiskAssessmentTool(BCRAT)combines1990-1996breastcancerincidenceforHispanicwomen with relative riskofriskfactorsfromNHW

&UpdatedBCRAT(RR from SEER 1993-1997breastcancerincidence)

age,ageatfirstlivebirth,ageatmenarche,numberoffirst-degreerelativeswithbreastcancer, number of breastbiopsiesandpresenceofatypicalhyperplasiaonapreviousbreastbiopsy

6,353Hispanicand128,976NHWpostmenopausalparticipantsaged50–79withoutahistoryofbreastcancerormastectomyinWHI

• BCRATHispanicswomenO/E=1.18,95%CI=0.99

to1.40;p=0.06)NHWwomen

O/E=1.18,95%CI=1.14to1.21;p<0.001).

(bothunderestimationby18%)

• UpdatedBCRATHispanicwomenO/E=

1.08,p=0.4NHWwomen

O/E=0.98,p=0.2

- Concordance statistic(AUC):0.63 (95%CI:0.58 to0.67) for Hispanicwomen

=0.58(95%CI:0.56to0.58)forNHWwomenintheWHI(p=0.03)


15

Performance statistics were reported by one study (Boggs 2013)38 and they found underestimation of breast cancer cases by 12% using the CARE model with modest discriminative accuracy with c-statistic of 0.57 (overall), 0.59 for Estrogen Receptor positive (ER+) breast cancer and 0.54 for Estrogen Receptor negative (ER-breast cancer). Summary of these studies are illustrated in Table 3.Boggs et al in 2013 conducted a prospective cohort validation study to assess the calibration and discriminatory accuracy of the CARE model among both younger and older black women and to assess the model’s predictive ability for specific breast cancer subtypes (estrogen receptor positive (ER+) and estrogen receptor negative (ER−) breast cancer) in US. The study involved 45,942 Black women (from Black Women’s Health Study) aged 30 to 69 years at baseline, with majority (41.6%) were between 30 to 39 years at baseline. The Black Women’s Health Study (BWHS) was an ongoing follow-up study of black women (1995) involving women aged 21 to 69. Women who had a history of cancer (n = 1859) or died (n = 64) before start of follow-up or with missing information on any risk factors in the model (n = 215) were excluded in the analysis of this study. CARE model factors were obtained from the BWHS baseline questionnaire. Self-administered baseline questionnaire were collected. Biennial follow-up questionnaires ascertain incident breast cancer (self report) from 1997 to 2005, which was confirmed by medical record or by cancer registry. Median age at diagnosis was 52 years. The mean length of follow up was 9.5 years (end of follow up was 31 December 2005). They found The CARE model underestimated the number of invasive breast cancers overall by 12% in this BWHS cohort with overall E/O ratio of 0.88 (95%CI=0.82 to 0.94), predicted and observed breast cancer cases were 749.6 and 852 cases respectively. CARE model showed underprediction of breast cancer risks among all categories of age, age at menarche, number of biopsies and number of affected relatives (E/O of 0.78-0.96, 0.84-0.91, 0.76-0.91 and 0.85-1.00), underprediction was greatest among women with previous biopsy, however the differences were not statistically significant. The discriminatory power of the CARE model in black women was modest with average age-specific concordance (c) statistics for total invasive breast cancer was 0.57 (95%CI 0.55 to 0.59). The result demonstrated that discrimination was worse for ER− breast cancer with average age-adjusted c statistics of 0.59 (95%CI 0.57 to 0.61) for ER+ breast cancer and 0.54 (95%CI 0.50 to 0.57) for ER− breast cancer.38

Adams-Campbell et al in 2009 conducted a cross sectional study to compare breast cancer risk estimates using the Gail model and the CARE model. The Gail model has been used to predict invasive breast cancer risk in women, however it underestimates risk in African-American women. The Contraceptive and Reproductive Experience (CARE) model has been developed to replace the Gail model in predicting breast cancer risk in African-American women. The study population completed risk assessment forms based on age at menarche, age at first live birth, number of affected relatives and number of previous benign biopsy examinations, between 2002 and 2005 to determine risk estimates. 5-year breast cancer risk was calculated for both the Gail and CARE models in the study subjects. The proportion of women with elevated breast cancer risk (risk ≥ 1.67%) was also compared. The sudy involved 883 African-American women undergone mammography in the breast cancer screening program at Howard University Cancer Center, Washington with mean age of 53.8±10.8 years (minimum 35 years). They found the mean 5-year breast cancer risk was higher in CARE model, 1.29% (range: 0.20 to 4.50%) compared to Gail model, 0.88% (range: 0.18 to 6.60%). Similarly, the proportion of women with elevated breast cancer risk (risk ≥ 1.67%) was higher using the CARE model (21%) compared to Gail model (7%) (p< 0.0001). A large difference of proportion of elevated breast cancer risk was observed for women ≥60 years of age between CARE model (51.7%) and Gail model (14.2%). The CARE model risk predictions were higher than those from the Gail model in all risk factor categories namely age at menarche, breast biopsy, age at first live birth; and in women 40 years and older, and those with at least one or no relatives with breast cancer.39

6.2.3 Other breast cancer risk prediction model

In this section, four studies reported development of new risk prediction model and subsequent validation which has been conducted in Italy40, US41,42 and Thailand.43

The validation population varies from Italian women aged 20 to 74 years 40, non Hispanic white women with median age of 52 years 41, African American women aged between 30 and 69 years (mean age 39.9)42 and Thailand women median aged 54 years.43


16

Three studies used different cohort from different study for model development and subsequent validation 40,41,43 whereas Boggs in 2015 used different cohort from a similar study for model development and validation.

Petracci included non-modifiable and modifiable risk factors (eight factors) in the new model, Pfeiffer developed model that share many hormonal and epidemiological risk factors; breast, endometrial and ovarian cancer in the US (nine factors), Boggs included nine risk factors in the BWHS model and Anothaisintawee included four variables/risk factors in the final model.

Performance statistics were reported by the four studies and they found the models were well calibrated with overall E/O ratio of 1.10,40 1.0041 and 0.9642 and O/E of 1.0043 for prediction of breast cancer cases with modest discriminative accuracy c-statistic of 0.65,43 0.62,40 0.58,41 and 0.5942. Summary of these studies are illustrated as in Table 4.

Petracci et al in 2010 conducted a prospective cohort study to estimate the effects of changes in modifiable risk factors on the absolute risk of breast cancer. In this study, a model was developed to predict the absolute risk of breast cancer using data from a multicentre case–control study of women in Italy (2569 case patients and 2588 control subjects studied from June 1, 1991, to April 1, 1994 involving six regions) and incidence and mortality data from the Florence Registries. Data from the case control study were used to select the non-modifiable and modifiable risk factors and to estimate the relative risks. The model included five non-modifiable risk factors (reproductive

Table 3: Description summary of risk prediction study characteristics related to performance of CARE model

Study author Design Model Risk factors Outcome

Study/ validated population

Validation statistic


Adams-Campbell 2009 39

Crosssectional

Contraceptive and Reproductive Experience(CARE)Model

• ageatmenarche,• ageatfirstlivebirth,• number of affected

relatives• numberofprevious

benignbiopsyexaminations

•5yearbreastcancerrisk0.88%

(Range:0.2-6.6%)(Gail model)

-1.29%(Range:0.2–4.5%)(CARE

model) • Proportionofwomen

withelevatedbreastcancerrisk(risk≥ 1.67%)

- 7%(Gailmodel)21%(CAREmodel)(p<0.0001).

- For women ≥60yearsofage,largedifferencesofproportionobserved;Gailmodel(14.2%)andCAREmodel(51.7%)

883African-Americanwomenwho participated in thebreastcancerscreeningprogramatHowardUniversityCancer Center, WashingtonMeanage:53.8±10.8years

- -

Boggs201338 Cohort CareMODEL AgeAgeatmenarcheNo.ofbiopsiesNoofaffectedrelatives

Validation 45,942Blackwomen(fromBlackWomen’sHealthStudy)aged30to69yearsatbaseline

Excluded:Women who had a historyofcancer(n=1859)ordied(n=64)beforestartof follow-up or with missinginformationonanyriskfactorsinthemodel(n=215),thisanalysisincluded45942.

Ageatbaseline=30to39years(41.6%;majority)

Medianageatdiagnosis=52years

CAREmodel:E/Oratio=0.88(95%CI=0.82to0.94)

Age-specificconcordancestatisticfortotalinvasivebreastcancer-0.57(95%CI=0.55to0.59)

Age-adjustedconcordancestatistics-0.59(95%CI=0.57to0.61)(ER+breastcancer)-0.54(95%CI=0.50to0.57)(ER−breastcancer)


17

Table 4: Description summary of risk prediction study characteristics related to performances of other models

Study author Design Model Risk factors

Outcome Study/ validated

population



Petracci201040 Cohort validation study

Petracci • Non-modifiable(five)andmodifiableriskfactors(three)

• Ageatmenarche• Ageatfirstlive

birth• No.ofaffectedfirst

degreerelative• No.ofbiopsies• Occupational

physicalactivitylevel

• Education• Leisure-time

physicalactivitylevel

• Alcoholintake• BMI

• Newmodel(multicentrecase–controlstudy1991to1994)

• Validation(Florence-EPICcohort1998to2004)

• Impactofreducing

modifiableexposuresonabsolutebreastcancerrisk

For45-year-oldMeanARR=0.6%(95%CI=0.3to1.0%)and1.4%(95%CI=0.7to2.0%)(for10and20yearsprojection)

For 65-year-old ARR=0.9%(95%CI=0.5to1.3%)and1.6%(95%CI=0.9to2.3%)(for10and20yearsprojection)(absoluteriskreduction fromexposuremodificationswasnearly proportional totheriskbeforemodifyingtheriskfactorsandincreasedwithageandriskprojectiontimespan)

Mean 20-year reductionsinabsoluterisk

- Womenaged65years:1.6%(95%CI:0.9to2.3%),

- Womenwithpositivefamilyhistory:3.2%(95%CI:1.8to4.8%),

- Womeninthehighest10%ofthetotalpopulationrisk4.1%(95%CI:2.5to6.8%)

• Model development :Multicentre case–controlstudy:2569cases(femalepatientswithbreastcancer,aged23to74years,medianage:55years);

controlsubjects(n=2588) were women aged20to74years(medianage:56years)withoutbreastcancer

• Modelvalidation:TheFlorence-EPICcohort included 10, 083womenaged35–64years

Overall:E/Oratio=1.10(95%

CI=0.96to1.26,p=0.19)

Othervariables:Themodelshowed

over-prediction ofbreastcancerrisksforthesesubgroups;

• Womenaged60yearsormore,E/Oratio=1.47(95%CI=1.03to2.09)

• Womenaged30ormoreatfirstlivebirth,

E/Oratio=1.72(95%CI=1.24to2.38)

• Women with 12 yearsormoreofeducation.

E/Oratio=1.34(95%CI=1.09to1.65)

c-statistic=0.62(95%CI:0.55to0.69)(womenyoungerthan50years)

c-statistic=0.57(95%CI:0.52to0.61)(womenaged50yearsandolder)


18

Study author Design Model Risk factors

Outcome Study/ validated

population



Pfeiffer201341 Cohort validation study

Pfeiffer • menopausehormone therapy (MHT)use

• otherMHTuse• ageatfirstlive

birth• menopausalstatus• ageatmenopause• familyhistory• benignbreast

disease/biopsy• alcohol• BMI

• Newmodel(NIH-AARP&PCLOstudy)

• Validation(NHSstudy)

• Breastcancerriskprojection

- Absoluteriskestimates=1.57%to21.78%(10yearprojection)

- Absoluteriskestimates=3.64%to35.11%(20yearprojection)

Finalstudypopulationincluded191,604(NIH-AARP)and64,440(PLCO)womenforbreastcanceranalysis

Formodelvalidation:Final validation included 57,906 womenfromtheNursesHealthStudy,aged30–55(forthebreastcancer model)

Restriction:Non-Hispanic,whitewomen who completed baselinequestionnaire,had follow-up information, and had nopersonalhistoryofthecancerofinterestatbaseline,

Ageatbaseline=30to39years(41.6%;majority)

Medianageatdiagnosis=52years

Newmodel:Overall

E/Oratio=1.00(95%CI:0.96to1.04)Significant

underestimationinpremenopausalwomen (30-55 years);

E/Oratio=0.73(95%CI:0.65to0.83)

BCRATmodelOverallE/Oratio=

1.0(95%CI0.97to1.04

Women above 55 years;E/Oratio=0.80(95%CI0.70to0.92)

Premenopausalwomen(aged30to55),E/Oratio=0.81(95%CI0.72to0.92).

OverallAUCintheNHScohort=0.58(95%CI:0.57to0.59).

Boggs201542 Cohort BWHSmodel

• Familyhistory• Previousbiopsy• Bodymassindexat

age18years• Ageatmenarche• Ageatfirstbirth• Oralcontraceptive

use• Bilateral

oophorectomy• Estrogenplus

progestinuse• Height

• Newmodeldevelopment(BWHS1995 to 2005)

• Validation(BWHS2006to2010)

Proportionofwomenwithelevatedriskofbreastcancer(5-yearpredictedriskof1.66%orgreater)&comparison

Womenyoungerthan50years

- 2.8%(BWHSmodel)vs0.1%(CAREmodel)Women more than 50

yearsold- 32.2%(BWHSmodel)vs7.3%(CAREmodel)Overall(womenage

30to69years)- 14.6%(BWHSmodel)

vs3.0%(CAREmodel)

55,879AfricanAmericanwomenfromtheBWHScohortaged30to69yearsatbaselineorwhoreachedage30yearsduringfollow-up,hadnohistoryofcanceratbaseline,andhadnomissingdataonvariablesinthefinalmodel

Meanage:39.9(1996cohort)and47.2(2006)

BWHSmodel:OverallE/Oratio=0.96(95%CI0.88to1.05)

Significantunderestimation:Women without a

familyhistoryofbreastcancer(E/Oratio=0.90;95%CI,0.81to0.99),

Nulliparouswomen(E/Oratio=0.83;95%CI0.70to0.99),

Women with recent (10years)useofOCP(E/Oratio=0.75;95%CI0.63to0.89)

Inthesecondlowestage-adjusted

quintileofrisk(E/Oratio=0.75;95%CI0.61to0.91)

AUC(overall)=0.59(95%CI0.56to0.61)

AUC=0.62;(95%CI0.58to0.65)(women youngerthan50years)

AUC=0.56;(95%CI0.53to0.59)(women more than50years)

AUC=0.58;(95%CI0.55to0.61)(women with ER+)

AUC=0.62;(95%CI0.58to0.66)(women with ER-)

Anothaisintaweeetalin2014

Crosssectional

Thaimodel

• age,• menopausalstatus,• bodymassindex

and • useoforal

contraceptives

• Model derivation• Model validation

(internal):usedbootstrapwith200repetitions

• Model validation (external)useddatafromSrinagarindandSongklanagarindHospitals

• Riskscore&riskstratification:low,low-intermediate, intermediate-high,andhigh-risk

17,506 women whom undertook mammographicscreeninginRamathibodiHospital(model derivation)

Womenundergoingmammographicscreeningaged18yearsandabovewereeligible.

Thosewithhistoryofinvasivebreastcancer,ductal carcinoma in situorothercancerswere excluded

O/Eratio=1.0(95%CI0.82to1.21)

Externalvalidation;O/Eratio0.97(95%CI0.68to1.35)

Overallc-statistic=0.65(95%CI0.59to0.70

Internalvalidation C-statistic=0.64(95%CI0.642to0.650)

External validationC-statistic=0.61(95%CI0.51to0.71)


19

characteristics, education, occupational activity, family history, and biopsy history) and three modifiable risk factors (alcohol consumption, leisure physical activity, and body mass index). The developed model was validated using independent data from the Italian cohort, Florence-EPIC cohort study from 1998 to 2004. This study involved 2569 cases (female patients with breast cancer, aged 23 to 74 years, median age: 55 years); and 2588 control subjects aged 20 to 74 years without breast cancer (median age: 56 years) from a case control study to develop the model and subsequently involved the Florence-EPIC cohort of 10, 083 women aged 35 to 64 years for model validation. The study found that the model was reasonably well calibrated with overall E/O ratio of 1.10 (95%CI 0.96 to 1.26, p=0.19). The model also showed over-prediction of breast cancer risks for other variables, women aged 60 years or more, women aged 30 or more at first live birth, and women with 12 years or more of education. The discriminatory accuracy was modest. It was demonstrated that the absolute risk reduction from exposure modifications was nearly proportional to the risk before modifying the risk factors and increased with age and risk projection time span. Mean 20-year reductions in absolute risk among women aged 65 years were 1.6% (95%CI 0.9 to 2.3%) in the entire population, 3.2% (95%CI 1.8 to 4.8%) among women with a positive family history of breast cancer, and 4.1% (95%CI 2.5 to 6.8%) among women who accounted for the highest 10% of the total population risk, as determined from the Lorenz curve. The author concluded that the new absolute risk prediction model for invasive breast cancer was reasonably well calibrated overall but overestimated the absolute risk of breast cancer in some subgroups such as women aged 60 years or older, and women whose first live birth occurred at age 30 years or later with modest discriminatory accuracy. These data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. The methods are also useful for calculating sample sizes required for trials to test lifestyle interventions.40

Pfeiffer et al in 2013 conducted a prospective cohort study to develop a model in predicting risk of cancers that share many hormonal and epidemiological risk factors; breast, endometrial and ovarian cancer. This study involved different cohorts for model development and validation. The absolute risk prediction model was developed for breast, endometrial, and ovarian cancer by combining data from two large prospective cohorts; the National Cancer Institute (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the National Institutes of Health–AARP Diet and Health Study (NIH-AARP)); and age-specific US population incidence and competing mortality rates from the NCI’s Surveillance, Epidemiology, and End Results Program (SEER). Study population for breast cancer analysis included 191,604 (NIH-AARP) aged 50 to 71 and 64,440 (PLCO) women aged 55 to 74. Model validation study population included 57,906 women from the Nurses Health Study, aged 30 to 55 (for the breast cancer model). Cancers were ascertained via annual study updates and death certificates, and verified via review of medical records (PLCO), through linkage with state cancer registries (NIH-AARP). Average follow up was 7.1years (starting one year after the entry mammogram, yielded 141,034 person years of follow-up). The final relative risk model for breast cancer included these risk factors; BMI, estrogen and progestin Menopause Hormone Therapy use, other MHT use, parity, age at first birth, premenopausal, age at menopause, benign breast diseases, family history of breast or ovarian cancer and alcohol consumption.41

The study found the new model is well calibrated with overall E/O ratio of 1.00 (95%CI 0.96 to1.04), however it significantly underestimated the number of breast cancers in premenopausal women (aged 30 to 55) with E/O ratio of 0.73 (95%CI 0.65 to 0.83). Peformance of BCRAT model showed it was well calibrated with E/O ratio of 1.0 (95%CI 0.97 to 1.04) and it significantly underestimated number of breast cancers in women above 55 years (E/O ratio of 0.80 (95%CI 0.70 to 0.92) and premenopausal women (aged 30 to 55) with E/O ratio of 0.81 (95%CI 0.72 to 0.92). Discriminatory power were modest for both new model and BCRAT in the NHS validation data with overall AUC of 0.58 (95%CI 0.57 to 0.59) and 0.56 (95% CI 0.55 to 0.58) respectively. Absolute risk estimates were 1.57% to 21.78% (for 10 year breast cancer projection) and 3.64% to 35.11% (20 year projection). The author concluded that these models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors among white, non-Hispanic women aged 50+ years. The models may assist in clinical decision-making related to the risks of these cancers and might improve the ability to identify potential participants for research studies. Limitations are the modest discriminatory ability of the breast and ovarian models, and that these models may not generalize to women of other races.41


20

Boggs et al recently in 2015 conducted a prospective validation study to develop a breast cancer risk prediction model for African American women using prospective data from the BWHS in follow-up from 1995 to 2005 and to validate the model in subsequent follow-up from 2006 to 2010, overall and by age and ER status. The BWHS is an ongoing follow-up study of African American women established in 1995 involving 59,000 African American women from across the US. A breast cancer risk model for the women was developed using relative risks (RR) of invasive breast cancer derived from 10 years of follow-up (1995 to 2005) of BWHS participants age 30 to 69 years at baseline by Cox proportional hazards models. In this study, an absolute risk was estimated using RRs and attributable risks from the BWHS model, age-specific SEER breast cancer rates for African American women (1994 to1998), and age specific competing mortality rates for African American women (1996 to 2000). Using the subsequent five years of follow-up data (2006 to 2010), calibration as the ratio of expected to observed number of breast cancers and discriminatory accuracy using the concordance statistic were evaluated.

Baseline questionnaire on BWHS risk factor were collected (age at menarche, height, weight at age 18 years, waist and hip circumference, educational attainment, family history of breast cancer, parity, age at first birth, duration of breastfeeding, oral contraceptive use, age at menopause, type of menopause, menopausal hormone use, previous diagnosis of benign breast disease). Incident diagnoses of breast cancer were ascertained through self-report on biennial follow-up questionnaires and, for non-respondents, through linkage with state cancer registries. Breast cancer deaths were identified through linkage with the National Death Index. They found the BWHS model was well calibrated overall with E/O ratio of 0.96 (95%CI 0.88 to 1.05), across a wide age range and across strata of risk factors. It significantly underestimated risk of breast cancer in these subgroups; women without a family history of breast cancer (E/O ratio of 0.90; 95% CI 0.81 to 0.99), nulliparous women (E/O ratio of 0.83; 95%CI 0.70 to 0.99), women who used oral contraceptives within the previous 10 years (E/O ratio of 0.75; 95% CI 0.63 to 0.89) and women in the second lowest age-adjusted quintile of risk (E/O ratio of 0.75; 95%CI 0.61 to 0.91). The discriminatory accuracy of the model remains modest but was higher for breast cancer overall and for ER-negative breast cancer than the discriminatory accuracy of a previous model with fewer risk factors. The predictive ability was best for younger women. The author concluded that the model was well calibrated overall with best predictive ability for younger women. The model may be a useful tool to identify women who may benefit from screening before age 50 years. The proportion of women predicted to meet the 1.66% cut point commonly used to determine eligibility for breast cancer prevention trials was greatly increased relative to previous models. It is unclear how well the model will perform in study populations with different distributions of risk factors; the calibration and discrimination of the BWHS model should be evaluated in independent studies.42

Anothaisintawee et al in 2014 conducted a cross sectional study to derive and validate breast cancer risk prediction model for Thai women. This study used data from Ramathibodi Hospital which involved 17,506 women whom undertook mammographic screening in the model derivation phase. Another data from Srinagarind and Songklanagarind Hospitals were used to externally validate the derived model. A bootstrap with 200 repetitions was applied for internal validation. Women undergoing mammographic screening aged 18 years and above were eligible and those with history of invasive breast cancer, ductal carcinoma in situ or other cancers were excluded. The study found four risk factors; age, menopausal status, body mass index and use of oral contraceptives were included in the model. C-statistic and O/E ratio were 0.65 (95%CI 0.59 to 0.70) and 1.0 (95%CI 0.82 to 1.21). Internal validation showed C-statistic of 0.64(95% CI 0.642 to 0.650) while external validation demonstrated O/E ratio and C-statistic of 0.97 (95% CI 0.68 to 1.35) and 0.61 (95% CI 0.51 to 0.71), respectively. The author also created risk scores which was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. They concluded that the study had created a Thai breast cancer risk prediction model with good calibration and fair discrimination performance. The additional risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.43


21

6.2.4 Comparison of breast cancer risk prediction models

Three studies reported comparison of risk prediction models in which two were systematic review14,15 and another one a prospective cohort study validating several models among white women above 70 years old in Vermont, US. 12

Performance statistics were reported by the three studies and they found the models were well calibrated with E/O ratio ranging from 0.87 to 1.12 from Anothaisintawee 2012); 1.13 (Gail 1 model), 0.95 (Gail 2 model) and 0.96 (Rosner & Colditz model) in the meta analysis performed by Meads 2012 for prediction of breast cancer cases. Modest discriminative accuracy was demonstrated with c-statistic of 0.54 to 0.55,2 0.6315 and 0.59 to 0.62. 14 Summary of these studies are illustrated as in Table 5.

Meads et al in 2012 conducted a systematic review without restriction on study type to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. This review included 17 studies proposing or validating a breast cancer prediction model in a general female population using multiple variables, at least one of which was a modifiable risk factor. Studies with breast cancer in men, or women who already had breast cancer or benign breast pathology when recruited, studies in high risk groups of women, such as with specific genetic mutations or who have close family relatives with breast cancer, models investigating single risk factor results such as mammography, assessing genetic risk factors only, assessing invasive techniques such as biopsies only, assessing family history of breast or ovarian cancer only and predicting genetic mutations rather than cancer, screening and early detection studies and Models published more than before 1985 were excluded. Evidence searched was conducted between November 2009 and June 2010. No specific quality assessment checklist used. Results were summarised qualitatively, and meta-analysis of model performance statistics was undertaken. The review summarised qualitatively studies describing a new risk prediction model containing at least one modifiable risk factor, that aimed to predict breast cancer in populations and in individual women. Modifiable risk factors that were included in one or more models were alcohol consumption, breast biopsy number, BMI or weight, condom use, exogenous hormone use (HRT, contraceptive pill), and physical activity. Risk factors only included in one model were condom use, family history of any cancer, physical activity and reproductive age period. The most commonly included risk

Table 5: Selected summary characteristics of studies with risk prediction model comparing several model performances

Study author Design Model compared Risk factors Included studies/ validated population Outcome

Meta analysis of validation measures


Meads201215 Systematicreview(norestrictiononstudytype)

EvidencesearchedbetweenNovember2009 and June 2010

Nospecificqualityassessment

17Models:

• Arne2009• Barlow 2006• Boyle2004• Chen 2006• Colditz &

Rosner2000• Cook 2009• Decarli2006• Gail 1989• Constantino

2010• Gail 2007• Novotny2006• Rosner1994• Rosner&

Colditz 1996• Rosner1994• Tice2008• Tyrer&

Cuzick 2005• Wacholder

2010

• Modifiableriskfactorsincluded

• Commonestriskfactorsincluded(age,ageatfirstlivebirthand/orageatsubsequentbirthsandfamilyhistoryofbreastcancer)

• Anyarticlesproposingorvalidatingabreastcancer prediction modelinageneralfemale population usingmultiplevariables,atleastoneofwhichwasamodifiableriskfactor

• Includedstudies:26Development

ofanewriskprediction model only=6

Both development of a new model and validation of one ormoremodels=11

Independentvalidation of one ormoremodels=9

- Qualitativesummary:• Studiesdescribinga

newriskpredictionmodel(17models)

• Modifiableriskfactorsincluded

• Commonestriskfactorsincluded

• Studiesvalidatingpredictionmodels(Colditz 2000, Gail 1 &Gail2,RosnerandColditz1994,TyrerandCuzick2004)

- Performancestatistics(metaanalysisofE/Oratioandc-statistic)

• Gail1Model:E/Oratio=1.13

(95%CI:0.80to1.60)

• Gail2Model:E/Oratio=0.95

(95%CI:0.88to1.01)

• Rosner&Colditzmodel

AverageE/Oratio=0.96(95%CI:0.92to1.02)

• Gail2Model:averageCstatistic

=0.63(95%CI:0.59to0.67)

• Colditz&RosnerModel:

averageCstatistic=0.63(95%CI:0.63to0.64)


22

Study author Design Model compared Risk factors Included studies/ validated population Outcome

Meta analysis of validation measures


Anothaisintawee2012 14

Systematicreview(observationalstudies)

Evidencesearchedbetween1949(MEDLINE),1974(EMBASE)andOct2010

Nospecificqualityassessment

• Variables/riskfactorsincluded:

- From 5 to 13 variables(age,ageatmenarche,ageatfirstlivebirth,familyhistoryofbreastcancer,numbersofpreviousbreastbiopsy,historyof atypical hyperplasia,breastdensity,nippleaspiratefluidcytology,race, family historyofanycancer,historyofbreastinflammation, bodymassindex, parity,ageatsubsequentbirth,ageatmenopause,benignbreastdisease,hormone replacement therapy, alcohol, serumestradiol)

Includedstudies:25

• Development/Modificationofpreviousprediction model =18

• Validation of existingpredictionmodels=7

-Qualitativesummary

-Studydescription;Amongthe18studiesdeveloping/modifyingmodels;• Studysample:from

550to2,404,636• Outcome-Predictingoverall

invasivebreastcancer(13studies)-Bothnon-invasive

(carcinomainsitu)andinvasivebreastcancer(fivestudies)

-Estimatingtheriskofdevelopingestrogen-typespecificbreastcancer(twostudies)

• Dataused- fromgeneralwomen

(i.e.,pre-andpost-menopausal(most)

- basedonpostmenopausaldataonly(twostudies)

• Validationsperformed:- Internalvalidation

-4studies(BoyleP2004,ChlebowskiRT2007,Barlow WE 2006, TiceJA2004)

- External validation –5models(Gail,CARE,modifiedGail,Rosner&Colditz,andmodifiedRosner&Colditz)

- calibration(mostofthemodels)werebetween0.87to1.12

-Discrimination:- Medianc-statistic

=0.63(0.53-0.66)insettingswheremodelsweredeveloped;

- Medianc-statistic=0.59(0.56-0.63)insettingswheremodelsadopted

VacekPM201112 Prospectivecohortstudy

- Gail model, -Ticemodificationof the Gail model, - Barlow Model- Vermont model

Cohort of 19,779Vermontwomenaged70 and older who hadamammogramintheVermontBreastCancer Surveillance Systembetween1996and 2001Inclusion- Notpreviously

diagnosedwithbreastcancer,didnotdeclinetiusetheir data

Excluded- Prevalentcancers,

womendiagnosedwith cancer

Majority(97.7%)werewhite,(54.6%)agedbetween70-74

Incidenceofbreastcancer

821developedbreastcancer(5.0%)

Riskscoreandbreastcancerrisk

Theriskscorescomputed from each ofthemodelsweresignificantlyassociatedwithbreastcancerriskintheCoxregressionanalyses(p<0.001).

However,theregressioncoefficients(ß’s) were all significantlylowerthan one(ß=0.31fortheGailmodel,ß=0.26fortheTicemodel,ß=0.50fortheBarlowmodelandß=0.54forthe Vermont model) indicatingthattheobservedincreasesinrelativeriskperunitoftheriskscorewere much lower than predicted by the models.

- DiscriminationC-statistics=

0.54(95%CI=0.52–0.56)forthe Gail model,

C-statistics=0.54(95%CI=0.51–0.56)fortheTicemodificationofthe Gail model,

C-statistics=0.55(95%CI=0.53–0.58)foramodel developed by Barlow

C-statistics=0.55(95%CI=0.53–0.58)foraVermont model


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factors in the models were age, age at first live birth and/or age at subsequent births and family history of breast cancer. Of these, models with independent validations were Colditz 2000, Gail 1 (4 studies) and Gail 2 (12 studies), Rosner and Colditz 1994 and Tyrer and Cuzick 2004. Performance statistics performed demonstrated modest discriminatory ability of Gail 2 Model with average c-statistic of 0.63 (95%CI 0.59 to 0.67) and Colditz & Rosner model with average c-statistic of 0.63 (95%CI 0.63 to 0.64). 15

Meta analysis of these models showed none of the models were significantly well calibrated with E/O ratio of 1.13 (95%CI 0.80 to 1.60) for Gail 1 Model, 0.95 (95% CI 0.88 to 1.01) for Gail 2 Model and 0.96 (95%CI 0.92 to 1.02) for Rosner & Colditz model. The author concluded there is insufficient information to distinguish the most accurate model or models consistently accurate enough for clinical practice due to variable reporting and few validation studies. Further studies are needed; in particular primary studies that conduct a comparative valuation of all of the models on the same dataset. Further validation studies of existing prediction models, research to identify new risk factors, and to develop model with better predictive ability is needed, alongside clearer reporting and continual validation of new models as they develop. 15

Anothaisintawee et al in 2012 also conducted a systematic review to systematically review the development and performance of existing prediction models to identify the most reliable model and indicate their strength and weakness for guiding future model development.

The review included observational studies (cohort, case control or cross sectional study) with these criteria; considered more than one risk factor in the prediction model simultaneously, had the outcome as breast cancer vs non breast cancer, applied any regression equation (Logistic, Poisson or Cox) to build up prediction model and reported each model ‘s performance (E/O ratio or c-statistic). Evidence searched was conducted between 1949 from MEDLINE (PubMed) and 1974 from EMBASE (Ovid) until October 2010. No specific quality assessment used. Results were summarised qualitatively and meta-analysis was not undertaken. The review summarised qualitatively 25 studies in which 18 were studies on development/modification of previous prediction model and 7 studies on validation of existing prediction models. The review summarized qualitatively and majority of the studies were predicting overall invasive breast cancer (13 studies) and most of the studies used data from general women (pre- and post-menopausal). Up to 13 risk factors were included in the models. The Gail and Rosner and Colditz were two models that were modified by many scholars. They found calibration of most of the models were between 0.87 to 1.12, with reported poor to fair discrimination (c-statistic ranges between 0.53 to 0.66 in internal validation and 0.56 to 0.63 in external validation). The author concluded that most models yielded relatively poor discrimination in both internal and external validation with fair calibration performance of most models. The author stated a need to develop a reliable risk prediction model for breast cancer in future. 14

Vacek et al in 2011 conducted a cohort study to determine the models usefulness as risk assessment tools for women aged 70 and older, (the original Gail model, the Tice modification of the Gail model, the Barlow Model & the Vermont model), to examine their prediction ability to diagnose breast cancer and to compare model performances. This study involved a cohort of 19,779 Vermont women aged 70 and older who had a mammogram in the Vermont Breast Cancer Surveillance System between 1996 and 2001, who were not previously diagnosed with breast cancer. Majority of subjects were white (97.7%) between 70 to 74 years old (54.6%). This study utilized data from the Vermont Breast Cancer Surveillance System (VBCSS) to assemble a cohort, obtain risk factor information and identify subsequent cases of breast cancer. Invasive and in situ breast cancers diagnosed before January 1, 2010 were identified using both the pathology information in the VBCSS and the diagnosis codes in the Medicare & Medicaid Services claims data. Risk score then computed based on each of the four risk models. They found a 5.0% incidence of breast cancer (821 developed breast cancer). The risk scores computed from each of the models were significantly associated with breast cancer risk in the Cox regression analyses (p<0.001). The regression coefficients (ß’s) were all significantly lower than one (ß = 0.31 for the Gail model, ß = 0.26 for the Tice model, ß = 0.50 for the Barlow model and ß= 0.54 for the Vermont model) indicating that the observed increases in relative risk per unit of the risk score were much lower than predicted by the models. Comparison of model performances showed the discriminative ability were modest among the four models with c-statistics of 0.54 (95%CI 0.52 to 0.56) for the Gail model, 0.54 (95%CI 0.51 to 0.56) for the Tice


24

modification of the Gail model, 0.55 (95%CI 0.53 to 0.58) for a model developed by Barlow and 0.55 (95%CI 0.53 to 0.58) for the Vermont model. The author concluded that the models are not useful for assessing risk in women aged 70 and older. The study stated that age-related attenuation of the effects of some risk factors makes the prediction of breast cancer in older women particularly difficult. New risk factors and biomarkers for breast cancer are needed as well as statistical models that allow their effects to vary with age. 12

6.3 SAFETY RELATED TO BREAST CANCER RISK PREDICTION MODELS

Only one study reported on safety or adverse event or unpleasant consequences related to breast cancer risk prediction model in term of its psychological effect namely anxiety. The other study reported subjective perception of belief in breast cancer risk prediction model. Summary is as illustrated in Table 6.

A randomized controlled trial conducted in Korea by Kye et al in 2012 aimed at exploring the psychological effect of health risk appraisal using epidemiological risk factor profile. In this study, participants were recruited through advertisements in the websites between October and November 2009. This study involved 60 eligible women aged between 30 to 48 years old without prior cancer diagnosis (median age were 38 years) who were randomized to either a numerical HRA or the HRA using personal risk factor list after completed a baseline interview. The HRA was done through telephone interview and the participants attended intervention sessions which consist of provision of HRA results, pre- and post-HRA assessment for psychological status, and counselling at one month after the interview. The variables measured in this study related to personal risk factor were height, weight, alcohol, vegetable consumption, past history of benign breast disease, menstruation and oral contraceptive pill (OCP). Risk factors for numerical HRA used the Gail model factors. Anxiety level was measured using Spielberger State Trait Anxiety Inventory YZ (Korean version). The study demonstrated that the mean anxiety level was slightly higher for trait (long term) anxiety with mean (SD) of 44.7(9.2) compared to state (temporary) anxiety with mean(SD) of 42.6(9.2). They also found women who had higher risk status, had an odd of having increased anxiety about five times greater than women who had lower risk status (OR 5.03, 95% CI 1.54 to 16.43, p=0.01). The author concluded that communicating the risk status by individual health risk appraisal service can induce psychological sequelae, especially in women having higher risk status. Hospitals, institutes, or medical schools that are operating or planning to operate the online health risk appraisal service should take side effects such as psychological sequelae into consideration. Further research on cancer risk communication strategies for HRA service are required to explore long term outcome and to evaluate more psychological outcome. 44

Subjective perception of belief in breast cancer risk prediction model was studied by Scherer et al in 2012. They conducted a cross sectional study to determine when and why women chose to disbelieve the personalized breast cancer risk numbers.In this study, women whose medical records indicated that they might be at above average risk of developing breast cancer were recruited from the Henry Ford Health System in Detroit and Group Health in Seattle. The subjects participated in an online program which presented tailored information about individual breast cancer risk after completing BCRAT questions and viewing decision aid on chemoprevention. They were presented with tailored absolute risk (BCRAT score) and estimate of risk reduction with chemoprevention. This study involved 690 women at or above average risk of developing breast cancer as estimated by the BCRAT(score range 1.7-19.1) age range 46 to 74 years with majority of the subjects were white (97.6%). The subjects were asked whether they believe the program was personalized and whether they believe the numbers. The BCRAT score was calculated using these factors: age, ethnicity, personal risk of breast cancer, age at first menses, age at first live birth, number of first degree relatives who have had breast cancer, history of breast biopsies. The study found 18.9% of study subjects were disagree with tailored risk (131 participants disagreed with both risk numbers). Among the documented reasons for disagreement (qualitative assessment) were their family history made them either more or less likely to develop breast cancer than their tailored risk number suggested (I have aunts that have had cancer); the risk number seemed too high or too low (It just seemed low, the percentage was low compared to my concern); health habits and lifestyle (I am in excellent health and I live an active healthy lifestyle) and others such as they thought the calculation failed to account for relevant personal information such as family history, medical history, lifestyle,


25

Table 6: Evidences concerning with safety/undesirable consequence of breast cancer risk prediction models

Study author Design

Study population Outcome

Kye SY 2012 Randomized trial n=60women

agedbetween30to48yearsoldwithoutpriorcancerdiagnosis

medianage:38years

• Averageanxietylevel• Mean(SD)of44.7(9.2)fortrait(longterm)anxiety,42.6(9.2)forstate(temporary)

anxiety

• Predictorforincreaseinanxiety- Anxietyscoreincreasedamongwomenwhohadhigherriskstatusin‘riskscore’group

atfollowupcomparedtobaseline,withmean(SD)atbaselineandfollowupwere42.8(8.5)and47.0(9.6)respectively,howeveritwasnotsignificant

- Riskstatuswastheindependentpredictorofincreaseofstateanxietyafterhealthriskappraisalintervention,afteradjustedforage,education,HRAtype,numeracy,pre-HRAstateanxietyandHRAtype.

- Womenwhohadhigherriskstatushadanoddofhavingincreasedanxietyabout5

timesgreaterthanwomenwhohadlowerriskstatus(OR5.03,95%CI1.54to16.43,p=0.01)

Scherer 2013 Crosssectional n=690women

-atoraboveaverageriskofdevelopingbreastcancerasestimatedbytheBreastCancerRiskAssessmentTool(BCRAT)(scorerange1.7-19.1)-agerange46to74years-97.6%werewhite

• Influenceofinterventiononbeliefsthattheprogramwaspersonalized- Women who received “detailed information” were more likely to feel that

theprogramwaspersonalizedcomparedtothosereceiving“standardinformation”;withmean(SD)of4.85(1.39)vs4.64(1.41),p=0.05respectively

• Disagreementwithtailoredrisk 131participantsdisagreedwithbothrisknumbers(18.9%)Reasonsfordisagreement(qualitativeassessment)

- Theirfamilyhistorymadethemeithermoreorlesslikelytodevelopbreastcancerthantheirtailoredrisknumbersuggested(Ihaveauntsthathavehadcancer)

- Therisknumberseemedtoohighortoolow(Itjustseemedlow,thepercentagewaslowcomparedtomyconcern)

- Healthhabitsandlifestyle(IaminexcellenthealthandIliveanactivehealthylifestyle).

- Others:Theythoughtthecalculationfailedtoaccountforrelevantpersonalinformationsuchasfamilyhistory,medicalhistory,lifestyle,hormonereplacementtherapyorunspecified.

Authors’conclusion:Thebenefitsoftailoredriskstatisticsmaybeattenuatedbyatendencyforpeopletobeskepticalthattheseriskestimatesapplytothempersonally.Decisionaidsmayprovideriskinformationthatisnotacceptedbypatients,butaddressingthepatients’personalcircumstancesmayleadtogreateracceptance

hormone replacement therapy or unspecified. The author concluded that the benefits of tailored risk statistics may be attenuated by a tendency for people to be skeptical that these risk estimates apply to them personally. Decision aids may provide risk information that is not accepted by patients, but addressing the patients’ personal circumstances may lead to greater acceptance.45

6.4 COST IMPLICATION

There were no retrievable evidences related to the cost-effectiveness analysis (CEA) or cost-utility analysis (CUA) of health risk assessment or risk prediction model for breast cancer.

In this review, we documented cost implication related to the development of health risk assessment or risk prediction model for breast cancer in Malaysia.

If the cancer risk prediction model used is the Gail model or BCRAT, then no cost shall be involved as the access is free. The BCRAT is available to all who have access to the internet at the URL http://www.cancer.gov/bcrisktool/. The source code for the BCRAT is available at http://www.cancer.gov/bcrisktool/download-source-code.aspx. However, some programming knowledge is required to implement the tool on a local computer or network. There is no application of the tool that can be downloaded in a “ready to use” state. Additionally, there are no restrictions or fees for using or redistributing the Breast Cancer Risk Assessment source code. However, NCI as the originator of the tool should be acknowledged and if the tool is changed in any way, it should be indicated that the new implementation is adapted from NCI’s BCRAT.46

However no information is retrieavable if a new model for breast cancer risk prediction tailored to our local population is to be developed. No information available on cost implication involved in validating the newly developed model in our setting.


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Validation of the prediction models in the population could use the BRCAPRO and BOADICEA as the main tools specifically for models identifying mutation probability. The most appropriate validation for these tools will be a population-based cohort of patients who have been tested for germ line mutations in BRCA1, BRCA2 and other genes. Estimated potential direct cost of a genetic test is approximately RM4,000 to RM12, 000 per patient with the lowest could be at RM500 per patient. To re-validate this in another cohort would be expensive and it would depend on where (and how much) genetic testing would be provided. At the minimum cost of RM500 per patient, with assumption that 1,000 patients will be in the cohort, the minimal direct cost for validation of this tool would be around RM500, 000. (Communication with cancer researcher)

The cost involved in conducting a prospective cohort validation study which involved setting up cohorts to collect prospective occurrence of cancer can be very costly and the actual cost will depend on number of participants and years of follow up. Estimated potential direct cost implicated in designing, developing, testing and commissioning an available risk prediction model into a health risk assessment module is given approximately at RM75,000. (Communication with Programme Officer, Health Education Division). This amount encompasses sum of the relevant activities, in which the details are as listed below:

Services: • System development (1 lot) RM50,000• Installation and configuration (1 lot) RM5,000• Training and Training of Trainer (1 lot) RM15,000• Documentation (1 lot) RM1,000 Software: • Portal Management Application and Content Management System (CMS) (1 lot) RM4,000

6.5 ORGANIZATIONAL

The goal of of breast cancer risk assessment is to personalize management strategies for all women with the aim to increase survival in high risk women and to decrease cost and complication in low risk women. Estimated risk yielded by prediction models enables the stratification of individuals or groups of individuals by these risks. High risk women according to Gail is women with five year risk of at least 1.67% and use of tamoxifen is associated with 49% reduction in invasive breast cancer. Women at low risk may be best served with standard mammography and health maintenance recommendations.19 Intervention according to risk group has been addressed by several guidelines. The American Cancer Society (ACS)47 recommends mammography screening for average risk women before the age of 40, and the US Preventive Service Task Force48 recommended screening to be initiated at the age of 50 for average risk women. In 2006, ACS recommended annual MRI screening be used as an adjunct to mammography screening among women with known BRCA mutation, first degree relatives of BRCA carriers who have not been tested and women with lifetime risk of breast cancer of 20 to 25% or greater as defined by models that are largely dependent on complete multigenerational family histories on the maternal and paternal side such as BRCAPRO, Claus and Tyrer-Cuzick models. The Gail model was not recommended for risk assessment for MRI screening in the ACS Guideline because it does not incorporate family history of breast or ovarian cancer in second degree relatives.49

NICE in its guideline on Familial Breast Cancer (2013) stated that when available in secondary care, use of a carrier probability calculation method with demonstrated acceptable performance (calibration and discrimination) such as BOADICEA and the Manchester Scoring System as well as family history is recommended to determine who should be offered referral to specialist genetic clinic. Meanwhile in a specialist genetic clinic setting, NICE guideline recommended similarly the use of a carrier probability calculation method with demonstrated acceptable performance (calibration and discrimination) such as BOADICEA and the Manchaster Scoring System to assess probability of a BRCA1 or 2 mutation. Cancer risk communication should be offered as a personal risk estimate and uncertainties about the estimate should also be informed.50


27

Computerized risk estimate using Gail model are available on the National Cancer Institute (NCI) website. The NCI website on Gail model is accessed 20,000 to 30,000 times per month.26 Other computerized risk estimates such as Claus and BRCAPRO models are available via the CancerGene program which needs internet access.25 Computerized risk estimate using any model requires computer literate patient and other computerized risk estimates available in the website needs internet access.

Prediction modelling research may distinguish three major phases namely developing and internally validating a prediction model; testing, adjusting or updating the model for other individuals (external validation); and assessing its impact on therapeutic management and patient outcomes.24 The conduct of this specialized research needs a dedicated team and expertise.

The goal of screening is to detect pre-symptomatic cancer early enough for effective treatment. Cancer risk assessment aims to characterize individuals’ chance of developing cancer over a specified period to guide decision making about future screening behaviour and adoption of risk reduction strategies. Among ethical issues arised was psychological harms resulted from being labelled ‘at risk’, and additional diagnostic procedure can artificially increase sense of risk.The ‘at risk’ label also has implication for future health care cost. Theoretically increase psychological distress from risk labelling may contribute to future non-adherence to screening and risk reduction recommendations. Uncertainty about risk and benefit of prevention option affect decision making in that depending upon their risk category patient may face difficult decision about managing or reducing risk. In some cases, the result may present women with information that contrast with previously held belief, requiring a period of acceptance and adjustment prior to consideration of preventive options.17

Breast cancer risk assessment and available intervention for prevention and early detection among women at increased risk seem to be underutilized in the US population. Only 48% had ordered or referred a patient for BRCA1/2 testing and only 18% had software programs to calculate breast cancer risk. 51 Breast cancer risk assessment, genetic testing and other interventions are utilized even less by racial and ethnic minority women, low income and uninsured women, likely contributing to higher rates of late stage diagnosis. 52 The lack of a single, simple and accessible data collection tool that can produce risk estimates used in various guidelines might hinder the widespread adoption of breast cancer risk assessment in primary care settings. As scientific knowledge improves on how to identifity women at increased risk of breast cancer, tools for risk assessment need to be updated, adapted and used to benefit largest number of women.50

Each of the models is derived using different data, study design and risk factors. Steps that may be used in future design and validation of new breast cancer risk prediction model were utilizing material available from either placebo arms of cohort or prior randomized, controlled trial or ongoing prospective observational studies where patients had been followed for a period up to 15 years after data collection to allow both univariate and multivariate analysis of risk factors. Nested case control study would provide the most efficient study design. Subsequent step will involve retrospective validation study and prospective validation study. Analysis of existing data offer possibility to identify risk factors that could be used to develop better risk prediction model.53 Improvements in the models require revalidation processes. Assessment of individual risk can be undertaken using a variety of models. Models are imperfect even within the fairly developed field of breast cancer risk assessment. Uncertainties associated with risk estimates should be addressed and informed particularly when clinical decision has serious consequences.25

There are a number of models available to predict breast cancer risks. The most appropriate validation for it will be a longitudinal study that is following a population attending screening and determining the sensitivity and specificity of the tool in predicting the individuals who develop breast cancer. This is quite difficult to be carried out in Malaysia at the moment since there is no population-based screening programme and there are few (if any) large enough longitudinal cohorts examining breast cancer. (Communication with breast cancer researcher)


28

Additional practical concern is the issue of insurability of women found to be at high risk of breast cancer. This matter has risen for the measurement of BRCA1 and BRCA2 genes in women. Future legislation may be developed to protect those found to be at high risk.54

The lack of a single, simple and accessible data collection tool that can produce risk estimatesmight hinder the adoption of breast cancer risk assessment in healthcare setting. As scientific knowledge of how to identify premalignant and malignant disease earlier in the disease improves, tool for risk assessment need to be updated, adapted and used to benefit the largest number of women. In addition to creating tools that will allow clinicians to generate risk estimates for multiple purposes efforts are needed to ensure all women have access to those potentially life saving interventions.49

7 DISCUSSION

There was neither HTA report nor guidelines on health risk assessment or risk prediction model for the detection of breast cancer.

The systematic review found a total of seven breast cancer risk prediction models containing risk factors without genetic component. Some have been independently validated, most notably the Gail model with four validations (by Schonfeld 2010,33 Chay 2012,37 Banegas 201236 and Vacek 201112).

We have attempted to compare the model performances qualitatively according to the encountered model.

For Gail model, there were limited fair level of evidence from the four studies (Schonfeld 2010, Chay 2012, Banegas 2012 and Vacek 2011). Two studies found underestimation of breast cancer cases by 13 to 18% using the Gail model, however upgrading/recalibration of the model demonstrated it was well calibrated as the E/O improved to 1.0333 and underestimated breast cancer cases with O/E of 1.08.36 Another study found the Gail model overestimated breast cancer risk among Singaporean women aged 50 to 64.37 The studies overall discriminative accuracy was modest with c-statistic ranges between 0.54 to 0.67.33,6,37,12 The validation population was mostly from US post-menopausal white women aged 50 to 79 years Hispanics and non-hispanic 33,36 and post menopausal Vermont women more than 70 years.12

For CARE model, performance statistics were reported by one study 13 and they found underestimation of breast cancer cases by 12% using the CARE model with modest discriminative accuracy with c-statistic of 0.57 (overall), 0.59 (ER+breast cancer) and 0.54 (ER-breast cancer).The validation population was African-American women aged range from 30 to 69 years.

For other models, four studies reported development of new risk prediction model and subsequent validation which has been conducted in Italy40 the US41,42 and Thailand.43 The validated population varies from Italian women aged 20 to 74 years,40 non-Hispanic white women with median age of 52 years 41 and African American women aged between 30 and 69 years (mean age 39.9).42 Two studies used different study cohort for model development and subsequent validation 40,41 whereas Boggs42 used different cohort from similar study for model development and validation. Petracci40 included non-modifiable and modifiable risk factors (eight factors) in the new model, Pfeiffer41 developed model that share many hormonal and epidemiological risk factors; breast, endometrial and ovarian cancer in the US (nine factors), and Boggs42 includes nine risk factors in the BWHS model. Performance statistics reported by the three studies and they found their model were well calibrated with overall E/O ratio of 1.10, 40 1.00 41 and 0.9642 for prediction of breast cancer cases with modest discriminative accuracy (c-statistic of 0.62,40 0.5841 and 0.5942 )

For the three studies studies reporting comparison of few risk prediction models, performance statistics demonstrated the models were well calibrated with E/O ratio ranging from 0.87 to 1.12 from Anothaisintawee 2012); 1.13 (Gail 1 model), 0.95 (Gail 2 model) and 0.96 (Rosner & Colditz model) in the meta analysis performed by Meads 2012 for prediction of breast cancer cases. Modest discriminative accuracy were demonstrated with c-statistic of 0.54-0.55, 12 0.63 15 and 0.59 to 0.62.14


29

With regard to safety, only one study with small study subjects reported on safety related to breast cancer risk prediction model in term of its psychological effect namely anxiety. They found women who had higher risk status had an odd of having increased anxiety about five times greater than women who had lower risk status (OR 5.03).44 The other study reported subjective perception of belief in breast cancer risk prediction model.45

There were two local studies published on validation of breast cancer prediction model; however these studies evaluate models predicting mutation probability using BOADICEA and Manchaster Scoring System conducted in Malaysia.54,55 These two studies demonstrated that the two models performed less well in our population in that it will underestimate the probability of mutation carriers. Therefore the use a lower cut-off may be considered in circumstances where decision to apply on our population been made.

A local study by Thirtagiri et al in 2007 evaluated BRCA1 and BRCA2 mutation and compared the accuracy of the BOADICEA and MSS. The study involved 187 breast cancer patients with either early onset breast cancer (age less than 40 years) or a personal and/or family history of breast or ovarian cancer recruited between 2003 and 2007 in University Malaya Medical Centre, Kuala Lumpur where full sequencing of BRCA1 and BRCA2 were carried out. The two prediction algorithms were evaluated. Mean age of diagnosis of all women was 43.8 years (range 22 to 78 years). The overall performance in discriminating between those with and without mutation were moderate as demonstrated by AUC of 0.74 (95%CI 0.67 to 0.81) and 0.82 (95%CI 0.75 to 0.88) for BRCA1, and 0.82 (95%CI 0.75 to 0.88) and 0.56 (95%CI 0.48 to 0.64) for BRCA2, for MSS and BOADICEA respectively. The AUC for combined BRCA1/2 was better for MSS compared to BOADICEA with AUC of 0.80 (95%CI 0.72 to 0.86) and 0.73 (95%CI 0.65 to 0.80) respectively. They found both models underestimated the number of mutation carriers in the cohort. They also found the score of greater than 15 for combined BRCA1/2 in MSS as an optimal threshold with sensitivity of 72%, specificity of 74%, PPV of 37% and NPV of 93%. They stated that testing could be offered to individuals with personal or family history of cancer if they have MSS score of 15 or above. Lower threshold could be used when resource is available.54

Another local study by Hassan N in 2014 also evaluated these two model performances among 1692 incident and prevalent breast cancer cases recruited to the Malaysian Breast Cancer Genetic Study between 2003 and 2012, of whom 665 with early age of onset and/or family history of breast cancer were screened for BRCA1/2 mutation. For 577 subjects where pathologic data were available the added values of ER negativity on the models were evaluated. They found at 10% threshold, AUC was similar for both models and higher for BRCA1 compared to BRCA2 (0.73 and 0.73 for BRCA1, and 066 and 0.64 for BRCA2 in BOADICEA and MMS respectively).The addition of ER status improved the AUC for BRCA1 from 0.73 to 0.81 in BOADICEA and to 0.79 in MMS. The addition of other pathologic features, pr and HER2 did not improve BRCA1 accuracy.55

Among the use of breast cancer risk prediction model is its use in stratifying risk among women hence subsequent intervention could be targeted for. Currently several risk stratification exist in our setting aiming at early detection of breast cancer. The Malaysia National Population and Family Development Board or better known as Lembaga Penduduk dan Pembangunan Keluarga Negara (LPPKN) categorise women as high risk if she has any one of these factors; family history of breast cancer, previous history of atypia on breast biopsy or previous history of breast or ovarian cancer or currently on hormone replacement therapy (criteria A), or any two of these factors; family history of breast cancer, nulliparous or women with first childbirth after 30 years, menarche before 12 years old, menopause after 55 years old or body mass index more than 30 (criteria B).56 This is in tandem with the recent Malaysian Guideline on National Breast Cancer Early Detection Programme indicating mammogram for high risk women (as criteria above) and those above 40 years old.57

The Malaysian CPG on Management of Breast Cancer on the other hand stratifies risk factor as low (RR between 1.0 and 1.4), moderate (RR between 1.5 and 2.0) and high (RR of more than 2.0).21 Currently, Malaysia is practicing opportunistic screening for breast cancer as its screening policy, targeting those women attending the wellness clinic, maternal and child health clinics and women attending outpatient clinic in MOH facilities. This involve three main activities for breast cancer screening namely breast self awareness, clinical breast examination and mammography


30

screening.7The prevalence of overall breast examination in Malaysia was 70.3% in the third National Health Morbidity Survey;57 with highest for Breast Self Examination (57.1%), Clinical Breast Examination (51.7%) and mammogram (7.5%). To further promote greater awareness among women to do mammogram screening for early detection of breast cancer, the Ministry of Women, Family and Community Development provided a RM50 subsidy for every mammogram done in private clinics and hospitals registered with LPPKN for those who meet the eligible criteria which includes high risk women with household income below RM5, 000.7 None of the available guidelines did document on risk assessment or strafication using any of the existing breast cancer risk prediction model.

Though risk assessment tools can place breast cancer risk in context for women at all risk levels and allow for more focused management recommendations on the basis of the risk, ultimately risk assessment is only an estimate.19 Nevertheless, people should be offered a personal risk estimate for their cancer risk and carrier probability as well as uncertainties of the information.51 Risk assessment tools can place breast cancer risk in context for women at all risk levels and allow focused management on the basis of this risk.19 It is important to note that the risk factors and breast cancer rates in Malaysia are different than the risks and rates used in the existing model such as Gail, which are not comparable. Therefore, any model would have to be recalibrated to Malaysian facts of breast cancer to make it applicable. Cancer risk prediction model needs continual validation to give meaningful risk estimate and ensure its applicability in the setting it will be used. The complexity to develop and subsequently validate any breast cancer risk prediction model specifically models without genetic component is reflected in the data required and availability of dedicated research expertise that is required to come out with a model with consistently outstanding performance. 7.1 LIMITATIONS

The systematic review of literature has several limitations. There were limited validation studies on non-caucasian study population specifically from Asian region on breast cancer risk prediction models without genetic risk factor. A sizable study in Singapore showed gross overestimation of risk. Generalizability of the retrieved evidence is certainly restricted. There is currently no quality assessment tool or standard assessment of risk of bias for risk prediction modelling and validation studies. Altman has recently published a list of criteria nevertheless it was for prognostic modelling study though some factors relevant to prediction models described include components of study design, study population, sample size, completeness of data, variables and presentation.58 Systematic review of risk prediction studies is rare and difficult. There is also currently no standard reporting for risk prediction studies such as CONSORT guidelines for RCT. Therefore, there are variable reporting style by authors and single study reporting multiple model performances simultaneously are rare. No studies on economic evaluations of risk prediction models retrieved. Although there was no restriction in language during the search but only English full text articles were included in the report.

8 CONCLUSION

There was sufficient good level of retrievable evidence for breast cancer risk prediction model. There were six models identified for predicting breast cancer risk with Gail model is the most widely studied and validated model in various population. The Gail model appeared to have good calibration in validation studies done cross-population, however there is considerable heterogeneity across studies. This model showed moderate performance in terms of discriminatory ability.

For other risk prediction models, there was insufficient good level of retrievable evidence with only one study each of those other models (CARE model, model by Petracci, model by Pfeiffer, Vermont model, model by Anothaisintawee and BWHS model). The models were well calibrated in the validated population however appeared modest in discriminating woman who will be having breast cancer, than for those who will not in the study population.

There was insufficient evidence on the safety related to cancer risk prediction models for the detection of women who will develop breast cancer. Although there was minor adverse psychological sequale reported among high risk women who demonstrated to be five times more likely to have increased anxiety, it may be considered relatively safe.


31

There was no retrievable evidence on economic evaluation of health risk assessment or risk prediction model for breast cancer, or cost implication involved in developing a new risk prediction model without genetic component for breast cancer retrieved. The cost involved in validating a model by a prospective cohort validation study could be very costly depending on the number of study participants and years of follow up. However potential direct cost implicated to the designing, developing, testing and commissioning of available one breast cancer prediction model was given approximately at RM75,000.

Cancer risk prediction models need continual validation to give meaningful risk estimate and to ensure its applicability in the setting it will be used. The complexity to develop and subsequently validate any breast cancer risk prediction model specifically models without genetic component is reflected in the necessary local data required and availability of dedicated research expertise to create a robust model with consistent performance.

9 RECOMMENDATION

Although the above review showed that the Gail model had good calibration and moderate discriminative ability, it is not suitable to be introduced as one of the strategy in the prevention of breast cancer under the Malaysian National Cancer Control Programme yet as it needs further validation to develop a well-fitted model that would have better predictive ability tailored to Malaysian population. In addition, this model needs continual validation to determine the consistency of its performance.

10 REFERENCES

1. World Health Organisation. International Agency for Research on Cancer. Globocan 2012; Estimated cancer incidence, mortality and prevalence worldwide in 2012. Available online at http://globocan.iarc.fr/Default.aspx accessed on 10 May 2014.

2. O Zainal Ariffin, IT Nor Saleha. National Cancer Registry Malaysia Report 2007, Cancer statistics: data and figure, Peninsular Malaysia. Ministry of Health, Malaysia. 2011

3. O Zainal Ariffin, MA Zainudin and IT Nor Saleha, eds. 2006. Malaysian cancer statistics – Data and figure Peninsular Malaysia 2006. Kuala Lumpur: National Cancer Registry, Ministry of Health Malaysia.

4. Ferlay J, Bray F, Parkin DM, Pisani P eds 2001. Globocan 2000. IARC Press, Lyon5. Yip CH, Ibrahim M. Epidemiology of breast cancer in Malaysia. Asian Pac J Cancer Prev

2006.7;7.6. Evans DGR & Howell A. Breast cancer risk assessment models. Breast Cancer Research

2007.9;213.7. Dahlui M, Ramli S, Bulgiba AM. Breast cancer prevention and control programs in Malaysia.

Asian Pacific Journal of Cancer Prevention 2011.12;1-4.8. Boughey JC, Hartmann LC, Anderson SS, et al. Evaluation of the Tyrer-Cuzick (International

Breast Cancer Intervention Study) Model for Breast Cancer Risk Prediction in Women With Atypical Hyperplasia. Journal of Clinical Oncology. 2010;28(22):3591-3596. doi:10.1200/JCO.2010.28.0784.

9. Parkin DM, Bray F, Ferlay J, et al. Global cancer statisticsa 2002. CA Cancer J Clin 2005.55;74-108

10. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 2001.358;9291;1389-99.

11. Martin AM, Weber BL. Genetic and Hormonal Risk Factors in Breast Cancer. J Natl Cancer Inst 2000;92:1126–35

12. Vacek PM, Skelly JM, Geler BM. Breast cancer risk assessment in women aged 70 and older. Breast Cancer Res Treat.2011;130:291–299.DOI 10.1007/s10549-011-1576-1


32

13. Ries LAG EM, Kosary CL. SEER cancer statistics review 2004 (1975-2001). National Cancer Institute. Accessed online at http://seer.cancer.gov/csr/1975_2001/.

14. Anothaisisntawee T, Teerawattananon Y, Wiratkapun C, et al. Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat 2012. 133;1-10.

15. Meads C, Ahmed I, Riley RD. A systematic review of breast cancer incidence risk prediction models with meta analysis of their performance. Breast Cancer Res Treat 2012. 132; 365-377

16. Bondy ML & Newman LA. Breast cancer risk assessment models, applicability to African-American women. Cancer supplement 2003.97(1); 230-4.

17. Anderson EE, Hoskins K. Individual Breast Cancer risk assessment in Underserved Populations: Integrating empirical Bioethics and Health Disparities Research. Journal of health care for the poor and underserved. 2012;23(40):10.1353/hpu.2012.0178. doi:10.1353/hpu.2012.0178.

18. Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: a workshop on development, evaluation and application. J Natl Cancer Inst 2005;97:715-23.

19. Domchek SM, Eisen A, Calzone K. Application of Breast Cancer Risk Prediction Models in clinical practice. J Clin Oncol 2003; 21:593-601.

20. Wood ME. A clinician’s guide to breast cancer risk assessment. How best to apply the various tools available to help assess a woman’s risk for breast cancer. Cancer genetics 2010. February.8(1);15-20.

21. Ministry of Health Malaysia. Clinical Practice Guidelines.Management of Breast Cancer 2010.MOH/P/PAK/212.10(GU)

22. Danaei G, Vander Hoorn S, Lopez AD, et al. Causes of cancer in the world: comparative risk assessment of nine behavioral and environmental risk factors. Lancet 2005.366;1784-93.

23. Korde LA & Gadalla SM. Cancer risk assessment for primary care physician. Prim Care 2009. 36(3);471-88.doi:10.1016/j.pop.2009.04.006.

24. Moons KGM, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating and impact assessment. Heart 2012. May;98(9):691-8. doi:10.1136/heartjnl-2011-301247

25. Claus EB 2001. Risk models used to counsel women for breast and ovarian cancer: a guide for clinicians. Familial Cancer 2001.1;197-206.

26. Elmore JG, Fletcher SW. The risk of cancer risk prediction: “What is my risk of getting breast cancer?” J Natl Cancer Inst. 2006;98:23:1673–1675

27. Canadian Breast Cancer Foundation. Risk assessment tool. Available at http://www.cbcf.org/central/aboutbreasthealth/preventionriskreduction/evaluatingbreastcancerrisk/pages/using-risk-assessment-tools.aspx. Accessed online on 10 January 2015

28. Fischer C, Kuchenbacker K, Engel, et al. Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium.2013;J Med Genet 50: 360–367.

29. Risk evaluator software. IBIS Breast Cancer Risk Evaluation Tool.Available at http://www.ems-trials.org/riskevaluator/ Accessed online on 15 January 2015

30. Critical Appraisal Skilled Programme Checklist.Making sense of evidence.Available at http://www.casp-uk.net/#!casp-tools-checklists/c18f8. Accesed online on 10 December 2014

31. Harris RP, Helfand M, Woolf SH, et al., for the Methods Work Group, third U.S. Preventive Services Task Force. Current methods of the U.S. Preventive Services Task Force: a review of the process. Am J Prev Med 2001;20(suppl 3):21-35.

32. Gail MH, Brinton LA, Byar DP et al Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. 1989 J Natl Cancer Inst 81: 1879–1886

33. Schonfeld SJ, Pee D, Greenlee RT, et al. Effect of Changing Breast Cancer Incidence Rates on the Calibration of the Gail Model. J Clin Oncol 2010. 28:2411-2417


33

34. Constantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. 2010. J Natl Cancer Inst 91(18):1541–1548

35. Newman LA 2010. Applicability of the Gail model for breast cancer risk assessment in Turkish female population and evaluation of breastfeeding as a risk factor. Breast Cancer Res Treat. 2010 Apr;120(2):425-6. doi: 10.1007/s10549-009-0614-8. Epub 2009 Dec 5.

36. Banegas MP, Gail MH, LaCroix A, et al. Evaluating breast cancer risk projections for Hispanic women. Breast Cancer Res Treat. 2012; February; 132(1): . doi:10.1007/s10549-011-1900-9.

37. Chay WY, Ong WS, Tan PH, et al.Validation of the Gail model for predicting individual breast cancer risk in a prospective nationwide study of 28,104 Singapore women. Breast Cancer Research 2012.14:R19

38. Boggs DA, Rosenberg L, Pencina MJ et al.Validation of a Breast Cancer Risk Prediction Model Developed for Black Women. J Natl Cancer Inst 2013.105:361–367

39. Adams-Campbell LL Makambi KH, Frederick WWI et al. Breast Cancer Risk Assessments Comparing Gail and CARE Models in African-American Women. Breast J. 2009; 15(0 1): S72–S75. doi:10.1111/j.15244741.2009.00824.x

40. Petracci E, Decarli A, Schairer C, et al. Risk Factor Modification and Projections of Absolute Breast Cancer Risk. J Natl Cancer Inst 2011;103:1–12

41. Pfeiffer RM, Park Y, Kreimer AR, et al. Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies. PLoS Med 2013. 10(7): e1001492. doi:10.1371/journal.pmed.1001492

42. Boggs DA, Rosenberg L, Adams-Campbell LL, and Palmer JR. Prospective Approach to Breast Cancer Risk Prediction in African American Women: The Black Women’s Health Study Model. J Clin Oncol 2015.33; p1-7. DOI: 10.1200/JCO.2014.57.2750

43. Anothaisintawee T, Teerawattananon Y, Wiratkapun C, et al.Development and Validation of a Breast Cancer Risk Prediction Model for Thai Women: A Cross-Sectional Study. Asian Pac J Cancer Prev 2014. 15 (16), 6811-6817

44. Kye SY, Park K, Park HG et al. Psychological impact of health risk appraisal of Korean women at different levels of breast cancer risk: neglected aspect of the web-based cancer risk assessment tool. Asian Pacific J Cancer Prev. 2012;13:437-441

44. Scherer LD, Ubel PA, McClure J et al. Belief in numbers: when and why women disbelieve tailored breast cancer risk statistics. Patient education and counseling. 2013;92:253-259

45. Cancer.gov Inquiry. Email communication with US National Cancer Institute.Inquiry:141219-000001. Email date 23 December 2015

46. Smith RA, Saslow D, Sawyer KA, et al. American Cancer Society American Cancer Society Guidelines for Breast Cancer Screening: Update 2003. 2003; 53(3), 141–169. DOI: 10.3322/canjclin.53.3.141

47. HD Nelson, Huffman LH, Fu R et al. Genetic Risk Assessment and BRCA Mutation Testing for Breast and Ovarian Cancer susceptibility: Systematic Evidence Review for the US Preventive Service Task Force. Ann Intern Med 2005. 143(5);362-379.

48. Ward EM and Smith RA. Integrating tools for breast cancer risk assessment, risk reduction and early detection. Cancer Epidemiol Biomarkers Prev 2010.19(10);2428029. DOI:10.1158/1055-9965.EPI-10-0835

49. NICE clinical guideline 164.Familial Breast Cancer. Classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer 2013. guidance.nice.org.uk/cg164

50. Guerra CE, Sherman M, Armstrong K. Diffusion of breast cancer risk assessment in primary care. J Am Board Fam Med 2009.22;272-9.

51. Hall MJ, Olopade OI. Disparities in genetic testing: thinking outside the BRCA box. J Clin Oncol 2006. 24;2197-203.


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52. Santen RJ, Boyd NF, Chlebowski RT, et al. Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. Endocrine related cancer 2007.14; 169-187.

53. Thirtagiri E, Lee SY, Kang P, et al. Evaluation of BRCA1 and BRCA2 mutations and risk prediction models in a typical Asian country (Malaysia) with a relatively low incidence of breast cancer. Breast Cancer Research 2008;10;R59(doi:10.1186/bcr2118)

54. Evaluating the performance of BOADICEA and Manchaster Scoring System for Predicting the risk of having a BRCA mutation in an Asian Breast Cancer Cohort. Current Oncology 2014. 5th Conference International Symposium on Hereditary breast and ovarian cancer.ppe358-e391.21(2)(ppe389)

55. LPPKN Report 2011. Subsidi kesan barah payu dara. Lembaga Penduduk dan Pembangunan Keluarga Negara (subsidized screening for breast cancer) updated 2011.Available from http://www.lppkn.gov.my/lppkn/index.php?option accessed on 10 January 2015

56. Bahagian Pembangunan Kesihatan Keluarga, Kementerian Kesihatan Malaysia. Garispanduan Program Pengesanan Awal Kanser Payudara Kebangsaan 2011.

57. National Health and Morbidity Survey (NHMS III) 2006.Women’s Health. Breast Health Awareness. Available at http://www.nih.gov.my/NHMS/abstracts_11.html accessed on 16 February 2015

58. Altman D 2009. Prognostic models: a methodological framework and review of models for breast cancer. Cancer Invest 2009. 27;235-243.


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Appendix 1

HIERARCHY OF EVIDENCE FOR EFFECTIVENESS STUDIES

DESIGNATION OF LEVELS OF EVIDENCE

I Evidence obtained from at least one properly designed randomized controlled trial.

II-I Evidence obtained from well-designed controlled trials without randomization.

II-2 Evidence obtained from well-designed cohort or case-control analytic studies, preferably from more than one centre or research group.

II-3 Evidence obtained from multiple time series with or without the intervention. Dramatic results in uncontrolled experiments (such as the results of the introduction of penicillin treatment in the 1940s) could also be regarded as this type of evidence.

III Opinions or respected authorities, based on clinical experience; descriptive studies and case reports; or reports of expert committees.

SOURCE: US/CANADIAN PREVENTIVE SERVICES TASK FORCE (Harris 2001)


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Appendix 2

PTK – FM – 02

HEALTH TECHNOLOGY ASSESSMENT (HTA) PROTOCOL HEALTH RISK ASSESSMENT (HRA) MODULE FOR BREAST CANCER

1. BACKGROUND INFORMATION

Breast cancer is the second most commonly diagnosed cancer worldwide. Among women worldwide, it is the most commonly diagnosed cancer which represents 1 in 4 of all cancers and the most common cause of cancer death (522,000 deaths in 2012). The incidence is rising with 1.7 million women were diagnosed with breast cancer in 2012 and mortality has increased by 14% since the 2008 estimates.1 Incidence rates is higher in more developed region, however mortality is much higher in less developed countries due to lack of early detection and access to treatment facilities.1

In Malaysia, the National Cancer Registry (NCR) 2007 reported that breast cancer was the most commonly diagnosed cancer in women with overall age standardized incidence rate (ASR) of 29.1 per 100,000 population.2 It was however higher than incidence in other developing countries with ASR of 20 per 100,000 population.3 About 30 to 40% of Malaysian women presented at a later stage of breast cancer (stage III and IV) compared to other counterparts in the developing countries.4

The increase has been said related to changes in dietary and reproductive pattern, urbanization and ageing population.5,6 Hence, affordable and effective approaches in cancer control are needed for early detection, diagnosis and treatment of breast cancer particularly in the less developed countries.1

The widely quoted general population lifetime risk of having breast cancer is one in eight to one in 12, and the risk in any given decade was never greater than one in 30. The proportion of all female deaths due to breast cancer per decade is never greater than 20% with the greatest proportion from the middle age group (35 to 55 years).7 For familial breast cancer, approximately five to ten percent of cases occur in women with Hereditary Breast Ovarian Cancer Syndrome (HBOC), which has mutation in BRCA1 and BRCA2 genes.8 Women with BRCA1 and BRCA2 mutation have a 50% to 80% lifetime risk of breast cancer. These women also have a 40% to 60% lifetime risk of having contralateral breast cancer.8 The organized breast cancer screening programmes using mammography has been well established in Europe and Northern America resulting in improved five year survival rate, as high as 89%.9 Providing breast cancer screening programme using mammography to every woman in most developing countries is not feasible, thus identifying women with relatively higher risk of developing breast cancer is a promising alternative.10

Prediction model of breast cancer is a mathematical equation designed to quantify the risk an individual woman would develop breast cancer in a defined period.10 Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions, as well as to counsel those at risk and determine eligibility for prospective prevention trial.11,12 All known breast cancer risk factors need to be assessed to evaluate its risk over time as accurately as possible including family history, hormonal and reproductive risk factors such as the use of exogenous hormones (estrogen and progesterone), endogenous hormonal factors (ages at menarche, menopause and first childbirth), and environmental risks such as alcohol intake, diet, exercise, obesity, as well as increase breast density.13 21% of all breast cancer deaths worldwide are attributable to alcohol use, overweight and obesity, and physical inactivity.14 Models containing modifiable risk factors are of particular interest to patients and those involved in reducing population incidence rates.11

Two main types of cancer risk assessment models are; model aimed at assessing breast cancer risk over time or to predict probability of being diagnosed with a cancer; and the other aimed at assessing mutation probability of an individual or carrying a gene mutation that predisposes to a particular cancer.5 Some available models are the Gail model, Claus model, Cuzick-Tyrer model, Breast and ovarian analysis of disease incidence and carrier estimation algorithm (BOADICEA), BRCAPRO, Couch, Shattuck Eiden, IBIS models and Manchaster scoring.5 The Gail model provides 5-year and lifetime risk estimate of having breast cancer and is accessible on the internet. The Claus model is useful for assessing breast cancer risk with a family history, similarly with BRCAPRO, BOADICEA and IBIS models which can produce both mutation probabilities and breast and ovarian cancer estimates.12


37

Identification of individual with a suspected heritable cancer syndrome (HBOC) has implications for evaluation and application of risk reducing options such as chemoprevention and prophylactic surgery.15 These underline the need for risk prediction model for breast cancer, not only to predict which women will develop the disease, but also to apply appropriate intervention and lifestyle measures in order to prevent the disease.5 Better understanding of these model performances, including the strengths and limitations is needed before applying them in clinical practice.

In Malaysia, currently there is no risk assessment model/health risk assessment (HRA) for breast cancer in predicting probability of being diagnosed with a cancer and probability of carrying a gene mutation that predisposes individual to a particular cancer in existing cancer control approaches. Hence, this review was requested by a Senior Principal Assistant Director, Health Education Division, Ministry of Health to review the evidence on breast cancer risk assessment model as a tool in enhancing early detection and diagnosis of breast cancer, towards facilitating the implementation of affordable and effective cancer control approach in the country.

2 POLICY QUESTION

i. In Ministry of Health, should a health risk assessment (HRA) model for breast cancer be introduced as one of the strategies in the prevention of breast cancer under the Malaysia National Cancer Control Programme?

ii. If HRA model for breast cancer be introduced, which HRA for model breast cancer should be adopted in Malaysia?

2.1 OBJECTIVES

i. To assess the effectiveness, predictive accuracy, strengths and weaknesses of retrieved breast cancer risk assessment/prediction models among women

ii. To assess the safety, organizational and ethical issues, and economic implications related to risk assessment/prediction models for breast cancer among women

2.2 RESEARCH QUESTIONS

i. What are the effectiveness, predictive accuracy, strengths and weaknesses of retrieved breast cancer risk assessment/prediction models among women

ii. What are the safety, organizational and ethical issues and economic implications related to the use of breast cancer risk assessment/prediction models among women

3 METHODS

Systematic reviews following the principles used by Cochrane Collaboration will be conducted to achieve the objectives of this review

3.1 SEARCH STRATEGY

i. Electronic database will be searched for published literatures pertaining to breast cancer risk assessment/prediction models among women, its performance, accuracy, benefits, strengths and weaknesses, implications

ii. The following databases will be used to carry out the search of evidence:- MEDLINE, EBM Reviews-Cochrane Database of Systematic Review, EBM-Reviews-Cochrane Central Register of Controlled Trials, EBM Reviews-Health Technology Assessment, EBM Reviews-Cochrane Methodology Register, EBM Reviews-NHS Economic Evaluation Database, Database of Abstracts of Reviews of Effects (DARE), PubMed, Horizon Scanning, INAHTA Database, HTA database and FDA database.

iii. Additional literatures will be identified from the references of the relevant articles. iv. Expert in this area will be contacted when necessary to get further information. v. Handsearching of evidence will be conducted if necessary to find unpublished evidence vi. General search engine might be used to get additional web-based information if there is no

retrievable evidence from the scientific databases. vii. There will be no limitation applied in the search such as year and language. viii. The detail of the search strategy will be presented in the appendix.


38

3.2 INCLUSION AND EXCLUSION CRITERIA

3.2.1 INCLUSION CRITERIA

• Population : Adultwomanmorethan18yearsold• Intervention : Breastcancerriskassessment/riskpredictionmodel/HealthRisk

Assessment models involving single or multiple risk factors• Comparators :-• Outcome : i. Performance of available breast cancer risk prediction models

in terms of its predictive accuracy - sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), calibration as measured by expected/observed ratio, discrimination as measured by AUC or c-statistic

ii. Strengths and weaknesses of retrieved risk prediction model (qualitative)

iii. Effectiveness/benefit of the breast cancer risk assessment/predictive model related to patient outcome as measured by detection rate, acceptance, awareness, receptiveness

iv. Organizational (operational, training, resources), ethical, legal and economic implication

• Study design : No restriction of study type. HTA report, Systematic Review, Randomised Controlled Trial (RCT), Non Randomised Controlled Trial, diagnostic accuracy studies, Observational Studies (Cohort study, Case Control study, Cross Sectional study with gold standard), and economic evaluation studies such as cost-effectiveness / cost-utility analysis.

• Full text articles published in English.

3.2.2 EXCLUSION CRITERIA

• Breastcancerprognosticmodelswillnotbeincludedinthisreview• Studieswiththesedesignwillbeexcluded:

i. Animal study ii. Narrative review iii. Laboratory study

•Studies on risk assessment/ risk prediction/ health risk assessment models for breast cancer with any genetic component as risk factor

•Studies in women who already had breast cancer when recruited or studies involving male subjects

•Non English full text article.

3.3 DATA EXTRACTION STRATEGY

Data will be extracted by a reviewer and checked by a second reviewer using a pre-tested data extraction form. Disagreements will be resolved through discussion. A third person, whose decision is final will be consulted when disagreements persists after discussion.

3.4 QUALITY ASSESSMENT STRATEGY/ASSESMENT OF RISK OF BIAS

The validity of the eligible studies will be assessed by two reviewers independently using Critical Appraisal Skill Programs checklists criteria according to the study designs. The quality of the evidence will be graded according to US/Canadian Preventive Services Task Force Grading System.


39

3.5 METHODS OF ANALYSIS/SYNTHESIS

Data will be summarized in evidence table. If the data is suitable for statistical pooling, meta-analyses of the main outcomes will be performed. Otherwise data for the outcomes will be reported narratively.

4. REPORT WRITING

Appendix 3

Search strategy

MEDLINE ® In progress and other Non-Indexed Citations and Ovid Medline® 1946 to present.

1 human mammary neoplasm*.tw.2 neoplasm* human mammary.tw.3 breast adj1 neoplasm*.tw.4 cancer of the breast.tw.5 cancer of breast.tw6 mammary carcinoma* human.tw.7 mammary neoplasm* human.tw.8 carcinoma* human mammary.tw.9 human mammary carcinoma*.tw.10 breast adj1 tumor*.tw.11 malignant neoplasm of breast.tw.12 breast adj1 neoplasm*.tw.13 mammary cancer.tw.14 malignant tumor of breast.tw.15 breast adj1 cancer.tw.16 breast carcinoma.tw.17 or/1-1618 assessment* risk.tw.19 benefit-risk adj1 assessment*.tw.20 benefit risk assessment.tw.21 benefit-risk assessment.tw.22 risk benefit assessment.tw.23 risk-benefit adj1 assessment*.tw.24 risks and benefits.tw.25 risk assessment*.tw.26 benefits and risks.tw.27 health risk assessment model.tw.28 risk prediction.tw.29 prediction model.tw30 or/18-2931 Female.tw.32 wom*n.tw.33 wom* group*.tw.34 group wom*.tw.35 Adult*.tw.36 or/31-35

37 17 and 30 and 36


40

EBM Reviews - Cochrane Database of Systematic Reviews

1 human mammary neoplasm*.tw.

2 neoplasm* human mammary.tw.

3 breast adj1 neoplasm*.tw.

4 cancer of the breast.tw.

5 cancer of breast.tw

6 mammary carcinoma* human.tw.

7 mammary neoplasm* human.tw.

8 carcinoma* human mammary.tw.

9 human mammary carcinoma*.tw.

10 breast adj1 tumor*.tw.

11 malignant neoplasm of breast.tw.

12 breast adj1 neoplasm*.tw.

13 mammary cancer.tw.

14 malignant tumor of breast.tw.

15 breast adj1 cancer.tw.

16 breast carcinoma.tw.

17 or/1-16

18 assessment* risk.tw.

19 benefit-risk adj1 assessment*.tw.

20 benefit risk assessment.tw.

21 benefit-risk assessment.tw.

22 risk benefit assessment.tw.

23 risk-benefit adj1 assessment*.tw.

24 risks and benefits.tw.

25 risk assessment*.tw.

26 benefits and risks.tw.

27 health risk assessment model.tw.

28 risk prediction.tw.

29 prediction model.tw

30 or/18-29

31 Female.tw.

32 wom*n.tw.

33 wom* group*.tw.

34 group wom*.tw.

35 Adult*.tw.

36 or/31-35

37 17 and 30 and 36


41

Pubmed

(“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“human”[All Fields] AND “mammary”[All Fields] AND “neoplasm”[All Fields])) AND (“humans”[MeSH Terms] OR “humans”[All Fields] OR “human”[All Fields]) AND (“mammary glands, human”[MeSH Terms] OR (“mammary”[All Fields] AND “glands”[All Fields] AND “human”[All Fields]) OR “human mammary glands”[All Fields] OR “mammary”[All Fields] OR “breast”[MeSH Terms] OR “breast”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “neoplasm”[All Fields]) OR “breast neoplasm”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“cancer”[All Fields] AND “breast”[All Fields]) OR “cancer of breast”[All Fields]) AND (“mammary glands, human”[MeSH Terms] OR (“mammary”[All Fields] AND “glands”[All Fields] AND “human”[All Fields]) OR “human mammary glands”[All Fields] OR “mammary”[All Fields] OR “breast”[MeSH Terms] OR “breast”[All Fields]) AND (“carcinoma”[MeSH Terms] OR “carcinoma”[All Fields] OR “carcinomas”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“human”[All Fields] AND “mammary”[All Fields] AND “neoplasm”[All Fields])) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“human”[All Fields] AND “mammary”[All Fields] AND “neoplasm”[All Fields])) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“human”[All Fields] AND “mammary”[All Fields] AND “carcinoma”[All Fields]) OR “human mammary carcinoma”[All Fields]) AND (“tumours”[All Fields] OR “neoplasms”[MeSH Terms] OR “neoplasms”[All Fields] OR “tumors”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “malignant”[All Fields] AND “neoplasm”[All Fields]) OR “breast malignant neoplasm”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “malignant”[All Fields] AND “neoplasm”[All Fields]) OR “breast malignant neoplasm”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “neoplasm”[All Fields]) OR “breast neoplasm”[All Fields]) AND (“breast”[MeSH Terms] OR “breast”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “neoplasm”[All Fields]) OR “breast neoplasm”[All Fields]) AND (“mammary glands, human”[MeSH Terms] OR (“mammary”[All Fields] AND “glands”[All Fields] AND “human”[All Fields]) OR “human mammary glands”[All Fields] OR “mammary”[All Fields] OR “breast”[MeSH Terms] OR “breast”[All Fields]) AND cancer.malignant[All Fields] AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“tumor”[All Fields] AND “breast”[All Fields]) OR “tumor of breast”[All Fields]) AND (“neoplasms”[MeSH Terms] OR “neoplasms”[All Fields] OR (“malignant”[All Fields] AND “tumor”[All Fields]) OR “malignant tumor”[All Fields]) AND breast.breast[All Fields] AND (“neoplasms”[MeSH Terms] OR “neoplasms”[All Fields] OR “cancer”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “carcinoma”[All Fields]) OR “breast carcinoma”[All Fields])AND (“Assessment”[Journal] OR “assessment”[All Fields]) AND (“risk”[MeSH Terms] OR “risk”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“benefit”[All Fields] AND “risk”[All Fields] AND “assessment”[All Fields]) OR “benefit risk assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“benefit”[All Fields] AND “risk”[All Fields] AND “assessment”[All Fields]) OR “benefit risk assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“benefit”[All Fields] AND “risk”[All Fields] AND “assessment”[All Fields]) OR “benefit risk assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“risk”[All Fields] AND “benefit”[All Fields] AND “assessment”[All Fields]) OR “risk benefit assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“risk”[All Fields] AND “benefit”[All Fields] AND “assessment”[All Fields]) OR “risk benefit assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“risks”[All Fields] AND “benefits”[All Fields]) OR “risks and benefits”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields] OR (“benefits”[All Fields] AND “risks”[All Fields]) OR “benefits and risks”[All Fields]) AND (“health”[MeSH Terms] OR “health”[All Fields]) AND (“risk assessment”[MeSH Terms] OR (“risk”[All Fields] AND “assessment”[All Fields]) OR “risk assessment”[All Fields]) AND model[All Fields] AND (“risk”[MeSH Terms] OR “risk”[All Fields]) AND prediction[All Fields] AND prediction[All Fields] AND model.[All Fields]


42

Appendix 4

CRITICAL APPRAISAL SKILLED PROGRAMME CHECKLIST

SYSTEMATIC REVIEW

CRITERIA ASSESSED

Selection of studies (relevant studies included?) Yes No Can’t tell

Assessment of quality of included studies? Yes No Can’t tell

If the results of the review have been combined, is it reasonable to do so? (heterogeneity)

Yes No Can’t tell

RCT

CRITERIA ASSESSED

Assignment of patients randomised?Yes No Can’t tell

Allocation concealment?Yes No Can’t tell

Patients, health workers, study personnel blind to treatment?Yes No Can’t tell

Intention to treat analysis?Yes No Can’t tell

Explanation of loss to follow-up?Yes No Can’t tell


43

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p(if

app

licab

le)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

1.M

eads

C,A

hmed

I,

Riley

RD.

Asy

stem

atic

revie

wof

bre

ast

canc

erin

ciden

ceri

sk

pred

ictio

nm

odels

with

m

etaan

alysis

oft

heir

perfo

rman

ce.B

reas

tCa

ncer

Res

Trea

t20

12.1

32;3

65-3

77.

Syste

mati

crev

iew(

nore

strict

ion

onst

udyt

ype)

Objec

tive:

Toid

entif

yand

evalu

ateth

eper

form

ance

ofp

redi

ction

m

odels

forb

reas

tcan

cert

hatc

ontai

nm

odifi

able

facto

rs

Inclu

ded

:An

yarti

clesp

ropo

sing

orva

lidati

nga

brea

stca

ncer

pre

dicti

onm

odel

ina

gene

ral

female

pop

ulati

onu

sing

mul

tiple

varia

bles

,atl

east

oneo

fwhi

chw

asa

mod

ifiab

leris

kfac

tor,

nore

strict

ion

onst

udyt

ype.

Exclu

sion:

Brea

stca

ncer

inm

en,

Wom

enw

hoal

read

yhad

bre

astc

ance

rorb

enig

nbr

east

path

olog

ywhe

nre

crui

ted

Stud

iesin

hig

hris

kgro

upso

fwom

en,s

uch

asw

ith

spec

ificg

eneti

cmut

ation

sorw

hoh

avec

lose

fam

ily

relat

ivesw

ithb

reas

tcan

cer

Mod

elsin

vesti

gatin

gsin

gler

iskfa

ctorr

esul

tssu

chas

mam

mog

raph

y,as

sess

ing

gene

ticri

sk

facto

rso

nly,

asse

ssin

gin

vasiv

etec

hniq

uess

uch

as

biop

sieso

nly,

asse

ssin

gfa

mily

hist

oryo

fbre

ast

oro

varia

nca

ncer

onl

yand

pre

dicti

ngg

eneti

cm

utati

onsr

ather

than

canc

er

Scre

enin

gan

dea

rlyd

etecti

onst

udies

Mod

elsp

ublis

hed

mor

etha

n25

year

spre

vious

ly(i.

e.be

fore

198

5)

Sens

itive

sear

chin

data

base

sinc

ludi

ngM

EDLI

NEan

dEM

BASE

was

cond

ucted

betw

een

Nove

mbe

r200

9an

dJu

ne2

010.

No

spec

ificq

ualit

yass

essm

entc

heck

list

used

.Res

ults

were

sum

mar

ised

quali

tative

ly,an

dwh

ere

poss

ible

meta

-ana

lysis

ofm

odel

perfo

rman

cest

atisti

cs

wasu

nder

taken

.

II-2

Artic

lesin

clude

din

this

syste

mati

crev

iew=

26

•De

velo

pmen

tofa

new

risk

pr

edict

ion

mod

elon

ly=

6

•Bo

th d

evelo

pmen

t of a

new

m

odel

and

valid

ation

of

oneo

rmor

emod

els=

11

•In

depe

nden

tvali

datio

nof

on

eorm

orem

odels

=9

--

-De

scrip

tion

of s

tudi

es:

Stud

iesd

escr

ibin

gan

ewri

skp

redi

ction

mod

el

17st

udies

dev

elopi

nga

new

riskp

redi

ction

mod

elwe

refo

und

cont

ainin

gat

leas

tone

mod

ifiab

leris

kfac

tor,

that

aimed

top

redi

ctbr

east

canc

erin

pop

ulati

onsa

ndin

indi

vidua

lwom

en

Them

odifi

able

riskf

acto

rsth

atwe

rein

clude

din

one

orm

ore

mod

els-

alcoh

olco

nsum

ptio

n,b

reas

tbio

psyn

umbe

r,BM

Ior

weig

ht,c

ondo

mu

se,e

xoge

nous

hor

mon

euse

(HRT

,con

trace

ptive

pi

ll),a

ndp

hysic

alac

tivity

.

Them

ostc

omm

only

inclu

ded

riskf

acto

rsin

them

odels

-age

,age

at

first

liveb

irth

and/

orag

eats

ubse

quen

tbirt

hsan

dfam

ilyh

istor

yof

bre

astc

ance

r

Ther

iskfa

ctors

onl

yinc

lude

din

one

mod

el-c

ondo

mu

se,f

amily

hi

story

ofa

nyca

ncer,

phy

sical

activ

ityan

dre

prod

uctiv

eage

per

iod

Valid

ation

sofp

redi

ction

mod

els

Mod

elsli

sted

with

inde

pend

entv

alida

tions

-Co

lditz

200

0,G

ail1

(4

artic

les)a

ndG

ail2

(12

artic

les),

Rosn

eran

dCo

lditz

199

4an

dTy

rera

ndC

uzick

200

4

Perfo

rman

cest

atisti

cs:

Meta

analy

siso

fc-s

tatist

ic-

Gail

2M

odel:

-

aver

ageC

stati

stic=

0.6

3(9

5%C

I:0.

59to

0.6

7)

- Co

lditz

&R

osne

rM

odel

:-

aver

ageC

stati

stic=

0.6

3(9

5%C

I:0.

63to

0.6

4)

•M

etaan

alysis

ofe

xpec

ted/o

bser

ved

(E/O

)rati

o:-

Gail

1M

odel

:-

aver

ageE

/Ora

tiow

as1

.13

(95%

CI:

0.80

to1

.60)

-

Gail

2M

odel

:-

aver

ageE

/Ora

tiow

as0

.95

(95%

CI:0

.88

to1

.01)

-

Rosn

er&

Col

ditz

:av

erag

eE/O

ratio

was

0.9

6(9

5%C

I:0.9

2to

1.

02)

The

auth

or c

oncl

uded

:Th

erei

sins

uffic

ienti

nfor

mati

onto

dist

ingu

ishth

emos

tacc

urate

mod

elor

m

odels

cons

isten

tlyac

cura

teen

ough

forc

linica

lpra

ctice

due

tova

riabl

erep

ortin

gan

dfew

valid

ation

stud

ies.

Furth

erst

udies

aren

eede

d;in

par

ticul

arp

rimar

ystu

dies

that

cond

ucta

co

mpa

rativ

evalu

ation

ofa

llof

them

odels

on

thes

amed

atase

t.Fu

rther

valid

ation

stu

dies

ofe

xistin

gpr

edict

ion

mod

els,r

esea

rch

toid

entif

ynew

risk

facto

rs,a

ndto

de

velo

pm

odel

with

bett

erp

redi

ctive

abili

tyis

need

ed,a

long

sidec

leare

rrep

ortin

gan

dco

ntin

ualv

alida

tion

ofn

ewm

odels

asth

eyd

evelo

p.

Meta

analy

sisd

one,

nos

ampl

esiz

ean

dqu

ality

asse

ssm

ent

App

endi

x 5

EV

IDEN

CE

TAB

LE

: EFF

EC

TIV

EN

ES

S (

AC

CU

RA

CY

) Q

UES

TIO

N

: IS

HE

ALT

H R

ISK

AS

SE

SS

ME

NT

(HR

A)/C

AN

CE

R R

ISK

AS

SE

SS

ME

NT/

RIS

K P

RE

DIC

TIO

N M

OD

ELS

FO

R B

RE

AS

T C

AN

CE

R

E

FFE

CTI

VE

(AC

CU

RA

TE) F

OR

TH

E D

ETE

CTI

ON

OF

BR

EA

ST

CA

NC

ER

PA

TIE

NTS

?


44

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

2.A

noth

aisin

tawee

T,T

eera

watta

nano

nY,

Wira

tkapu

nC

etal.

Ri

skp

redi

ction

mod

els

ofb

reas

tcan

cer:

asy

stem

aticr

eview

of

mod

elpe

rform

ance

s.Br

east

Canc

er

ResT

reat.

2012

;133

;1-

10

Syste

mati

crev

iew(

obse

rvati

onal

studi

es)

Objec

tive:

Tosy

stem

atica

llyre

view

thed

evelo

pmen

tand

pe

rform

ance

ofe

xistin

gpr

edict

ion

mod

elsto

iden

tify

them

ostr

eliab

lem

odel

and

indi

cate

their

stre

ngth

and

weak

ness

forg

uidi

ngfu

ture

mod

elde

velo

pmen

t

Meth

odIn

clude

d:

Obse

rvati

onal

studi

es(c

ohor

t,ca

seco

ntro

lorc

ross

se

ction

alstu

dy)p

ublis

hed

inE

nglis

hm

eett

hese

crite

ria;

-Con

sider

edm

oret

han

oner

iskfa

ctori

nth

epre

dicti

on

mod

elsim

ultan

eous

ly,

-had

theo

utco

mea

sbre

astc

ance

rvsn

onb

reas

tcan

cer,

-app

lied

anyr

egre

ssio

neq

uatio

n(Lo

gisti

c,Po

isson

or

Cox)

to b

uild

up

pred

ictio

n m

odel,

-r

epor

tedea

chm

odel

‘spe

rform

ance

(E/O

ratio

or

c-sta

tistic

).

Them

ethod

pre

dicti

veab

ility,

calib

ratio

nan

ddi

scrim

inati

onw

eree

xtrac

ted,u

sing

E/O

ratio

and

ROC

analy

siso

rthe

conc

orda

nces

tatist

icre

spec

tively

.

MED

LINE

(Pub

Med

)fro

m1

949

and

EMBA

SE(O

vid)

were

sear

ched

from

197

4un

tilO

ctobe

r201

0.

II-2

Pape

rsin

clude

din

this

syste

mati

cre

view

=25

•De

velo

pmen

t/Mod

ifica

tion

of

prev

ious

pre

dicti

onm

odel

=18

•Va

lidati

ono

fexis

ting

pred

ictio

nm

odels

=7

--

-St

udy

desc

riptio

n;

Amon

gth

e18

studi

esd

evelo

ping

/mod

ifyin

gm

odels

;•

Stud

ysam

ple:

from

550

to2

,404

,636

•Ou

tcom

e-

Pre

dicti

ngo

vera

llin

vasiv

ebre

astc

ance

r

(13

stu

dies

)-

Bo

thn

on-in

vasiv

e(ca

rcin

omai

nsit

u)an

d

inva

siveb

reas

tcan

cer(

five

studi

es)

-

Estim

ating

ther

isko

fdev

elopi

nges

troge

n-t

ypes

pecifi

cbre

astc

ance

r(tw

ostu

dies

)•

Data

used

- fro

mg

ener

alwo

men

(i.e.

,pre

-and

pos

t-men

opau

sal

(mos

t)-

base

don

pos

tmen

opau

sald

atao

nly(

two

studi

es)

•Va

lidati

onsp

erfo

rmed

:-

Inter

nalv

alida

tion

-4st

udies

(Boy

leP

2004

,Chl

ebow

skiR

T20

07,B

arlo

wW

E20

06,T

iceJA

200

4)-

Exter

nalv

alida

tion

–5

mod

els(G

ail,C

ARE,

mod

ified

Gail

,Ros

ner&

Co

lditz

,and

mod

ified

Ros

ner&

Col

ditz)

•Va

riabl

es/ri

skfa

ctors

inclu

ded

:-

From

5to

13

varia

bles

(age

,age

atm

enar

che,

age

atfir

stliv

ebi

rth,

family

hist

oryo

fbre

astc

ance

r,num

bers

ofp

revio

usb

reas

tbio

psy,

histo

ry

ofat

ypica

lhyp

erpl

asia,

bre

astd

ensit

y,ni

pple

aspi

rate

fluid

cyto

logy

,rac

e,fam

ilyh

istor

yof

any

can

cer,

histo

ryo

fbre

asti

nflam

mati

on,b

ody

mas

sin

dex,

parit

y,ag

eat

subs

eque

ntb

irth,

age

atm

enop

ause

,ben

ign

brea

stdi

seas

e,ho

rmon

erep

lacem

entt

hera

py,a

lcoho

l,se

rum

estra

diol

)

Mod

el p

erfo

rman

ce:

Disc

rimin

ation

:-

Med

ianc

-stat

istic

=0.

63(0

.53-

0.66

)in

sett

ings

whe

rem

odels

wer

ede

velo

ped;

- M

edian

c-sta

tistic

=0

.59(

0.56

-0.6

3)in

setti

ngsw

here

mod

elsad

opted

.

Auth

orco

nclu

sion:

M

ostm

odels

yiel

ded

relat

ively

poor

disc

rimin

ation

inb

oth

inter

nala

nde

xtern

alva

lidati

on.

Ther

eis

still

ane

edto

dev

elop

are

liabl

eris

kpr

edict

ion

mod

elfo

rbr

east

canc

erin

futu

re.


45

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

3.A

dam

s-Ca

mpb

ell

LLM

akam

biK

H,

Fred

erick

WW

Iet

al.B

reas

tCan

cer

Risk

Ass

essm

ents

Com

parin

gGa

ilan

dCA

REM

odels

in

Afric

an-A

mer

ican

Wom

en.B

reas

tJ.

2009

;15(

01)

:S72

–S7

5.d

oi:1

0.11

11/

5244

741.

2009

.008

24.x

WAS

HING

TON

Cros

ssec

tiona

l

Objec

tive:

Toco

mpa

reb

reas

tcan

cerr

iskes

timate

sfro

mth

eGail

mod

elan

dth

eCAR

Em

odel

(tod

eterm

ine

ifth

eCA

REm

odel

gene

rates

hig

her5

-yea

rbr

east

canc

erri

skes

timate

sfor

Afri

can-

Amer

ican

wom

en

than

the G

ail m

odel)

Meth

odGa

ilmod

elha

sbee

nuse

dtop

redi

ctin

vasiv

ebre

astc

ance

rris

kin

wom

en,h

owev

erit

und

eres

timate

srisk

inA

frica

n-Am

erica

nwo

men

.The

Con

trace

ptive

and

Rep

rodu

ctive

Ex

perie

nce

(CAR

E)m

odel

has

been

dev

elope

dto

repl

ace

theG

ailm

odel

inp

redi

cting

bre

astc

ance

rrisk

inA

frica

n-Am

erica

nwo

men

.As

ampl

eof

Afri

can-

Amer

ican

wom

en

unde

rgon

em

amm

ogra

phy

com

plete

dris

kas

sess

men

tfo

rms

base

don

age

atm

enar

che,

age

atfir

stliv

ebi

rth,

num

ber

ofa

ffecte

dre

lative

san

dnu

mbe

rof

pre

vious

be

nign

bio

psy

exam

inati

ons,

betw

een

2002

and

200

5to

dete

rmin

eris

kes

timate

s.

5-ye

arb

reas

tcan

cerr

isk

was

calcu

lated

forb

oth

the

Gail

and

CARE

mod

elsin

the

study

sub

jects.

The

pro

porti

ono

fwom

enw

ithe

levate

dbr

east

canc

erri

sk(r

isk≥

1.6

7%)i

nth

em

odels

was

also

co

mpa

red.

III88

3Af

rican

-Am

erica

nwo

men

wh

opa

rticip

ated

inth

ebre

ast

canc

ersc

reen

ing

prog

ram

at

Howa

rdU

nive

rsity

Can

cerC

enter

,W

ashi

ngto

n

•M

ean

age:

53.

8±10

.8ye

ars

(min

imum

35

year

s)

•Br

east

canc

erri

sk

Mea

n5-

year

bre

astc

ance

rrisk

;=

0.88

%(R

ange

:0.1

8–6.

60%

)(Gail

mod

el)

=1.2

9%(R

ange

:0.2

0–4.

50%

)(CA

REm

odel)

•P

ropo

rtion

ofw

omen

with

elev

ated

brea

stca

ncer

risk

(risk

≥1

.67%

)

- 7%

usin

gth

eGail

mod

elco

mpa

red

to2

1%u

sing

theC

ARE

mod

el(p

<0.

0001

).-

For

wom

en ≥

60y

ears

of

age,

large

diff

eren

ces

ofp

ropo

rtion

ob

serv

ed;G

ailm

odel

(14.

2%)a

ndC

ARE

mod

el(5

1.7%

)

•Ri

skp

redi

ction

byc

atego

ry

•Th

eCA

REm

odel

risk

pred

ictio

nsw

ere

high

erth

anth

ose

from

the

Gail

mod

elin

allc

atego

ries:

(age

atm

enar

che,

brea

stbi

opsy

,age

atfi

rstl

iveb

irth;

and

inw

omen

40

yea

rsa

ndo

lder,

and

thos

ewi

tha

tlea

ston

eor

no

relat

ives

with

br

east

canc

er).

Auth

orco

nclu

sion:

The

CARE

mod

elris

kpr

edict

ions

wer

ehi

gher

than

thos

efro

mth

eGa

ilm

odel

for

Afric

an-A

mer

icanw

omen

.Use

ofC

AREm

odel

hass

igni

fican

timpl

icatio

nreg

ardi

ng

coun

selli

ngin

Afri

can-

Amer

ican

wom

ena

tinc

reas

edr

iskfo

rde

velo

ping

bre

ast

canc

er,a

ndin

dica

tesm

ore

Afric

an-A

mer

ican

wom

enw

ould

be

eligi

ble

forb

reas

tca

ncer

che

mop

reve

ntio

nstu

dya

ndth

eyc

ould

be

cons

ider

edfo

rpha

rmac

eutic

als

with

FDA

appr

oved

indi

catio

nfo

rbre

astc

ance

rred

uctio

n.


46

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

4.B

oggs

DA,

Ro

senb

erg

L,P

encin

aM

Jeta

l.Va

lidati

ono

faB

reas

tCa

ncer

Risk

Pre

dicti

on

Mod

elDe

velo

ped

for

Blac

kWom

en.

JNatl

Can

cer

Inst;

2013

;105

:361

–367

US

Valid

ation

stud

y(Pr

ospe

ctive

coho

rt)

Objec

tive:

Toa

sses

sth

eca

libra

tion

and

disc

rimin

atory

acc

urac

yof

th

eCA

REm

odel

amon

gbo

thy

oung

era

ndo

lder

blac

kwo

men

and

toa

sses

sth

em

odel’

spr

edict

ivea

bilit

yfo

rsp

ecifi

cbr

east

canc

ers

ubtyp

es(

estro

gen

rece

ptor

–po

sitive

(ER

+)a

nde

strog

enr

ecep

tor–

nega

tive

(ER−

)br

east

canc

er)

Meth

odTh

eBlac

kWom

en’s

Healt

hStu

dy(B

WHS

)was

anon

goin

gfo

llow-

upst

udyo

fblac

kwom

en(1

995)

invo

lving

wom

en

aged

21

to6

9.S

elf-a

dmin

ister

edb

aseli

neq

uesti

onna

ire

were

colle

cted.

CA

REm

odel

facto

rsw

ere

obtai

ned

from

the

BW

HS

base

lineq

uesti

onna

ire.

Bien

nial

follo

w-up

qu

estio

nnair

es

asce

rtain

in

ciden

tbr

east

canc

er(

self

repo

rt)f

rom

199

7to

200

5,w

hich

wa

sco

nfirm

edb

ym

edica

lrec

ord

orb

yca

ncer

regi

stry.

Analy

sisre

strict

edto

wom

en3

0ye

arso

rold

erat

thes

tart

offo

llow-

upo

nJa

nuar

y199

6.

II-2

45,9

42B

lack

wom

en(

from

Blac

kW

omen

’sHe

alth

Stud

y)a

ged

30to

69

year

satb

aseli

ne

Exclu

ded:

W

omen

wh

oha

da

histo

ry

of

canc

er(n

=1

859)

ord

ied(n

=6

4)

befo

res

tart

off

ollo

w-up

or

with

m

issin

gin

form

ation

on

any

risk

facto

rsin

them

odel

(n=

215

),th

isan

alysis

inclu

ded

459

42.

Age

atba

selin

e=

30to

39

year

s(4

1.6%

;majo

rity)

Med

iana

gea

tdi

agno

sis=

52

year

s

Valid

atio

n of

CAR

E m

odel

;•

Calib

ratio

n:-

Pred

icted

bre

astc

ance

rcas

es(C

ARE

mod

el)=

749

.6-

Obse

rved

inva

siveb

reas

tcan

cerc

ases

=8

52ca

ses

- E/

Ora

tio=

0.8

8(9

5%CI

=0.8

2to

0.9

4)

CARE

mod

elfac

tors

Risk

facto

rNo

.pre

dicte

dNo

.obs

erve

dE/

O(9

5%CI

)Ag

e 30-3

915

6.6.

183

0.86

(0.7

4-0.

99)

40-4

930

8.4

322

0.96

(0.8

6-1.

07)

50-5

919

0.2

245

0.78

(0.6

9-0.

88)

60-6

994

.310

20.

93(0

.76-

1.12

)Ag

eatm

enar

che

<12

213.

825

50.

84(0

.74-

0.95

)12

-13

409.

345

20.

91(0

.83-

0.99

)>1

412

6.6

145

0.87

(0.7

4-1.

03)

No.o

fbio

psies

062

2.9

685

0.91

(0.8

4-0.

98)

>112

6.7

167

0.76

(0.6

5-0.

88)

Noo

faffe

cted

relat

ives

062

5.2

707

0.88

(0.8

2-0.

95)

111

5.5

136

0.85

(0.7

2-1.

00)

29.

09

1.00

(0.5

2-1.

92)

CARE

mod

elsh

owed

und

erpr

edic

tion

ofbr

east

canc

erri

sksa

mon

gallc

atego

ries

howe

verw

ere

nots

tatist

ically

sig

nific

ant(

unde

rpre

dicti

ong

reate

stam

ong

wom

en

with

pre

vious

bio

psy)

.

Facto

rsn

otin

CAR

Em

odel

- Gre

atest

unde

rpre

dicti

ono

fbre

astc

ance

rrisk

am

ong

wom

ena

t25

year

sor

old

erat

birt

hof

firs

tchi

ld(E

/O=0

.79,

95%

CI=6

3to

0.8

1,p

=0.0

02).

Brea

stca

ncer

incid

ence

rates

- Ag

espe

cificb

reas

tcan

ceri

ncid

ence

rates

use

din

theC

ARE

mod

el(S

EER

prog

ram

)wer

e lo

wer

than

thos

ein

BWHS

;stan

dard

ized

incid

ence

ratio

(SIR

)was

0.8

8(9

5%C

I=0

.82

to0

.94)

•Di

scrim

inato

ryac

cura

cy

Aver

age

age-

spec

ific

conc

orda

nce

(c)

statis

ticfo

rto

talin

vasiv

ebr

east

canc

erw

as0

.57

(95%

CI=

0.5

5to

0.5

9).

The

aver

age

age-

adju

sted

conc

orda

nce

statis

tics

were

0.5

9(9

5%C

I=

0.57

to0

.61)

forE

R+b

reas

tcan

cera

nd0

.54

(95%

CI=

0.5

0to

0.5

7)fo

rER

−br

east

canc

er.

Auth

orco

nclu

sion:

The

CARE

mod

elun

dere

stim

ated

the

num

bero

finv

asive

bre

astc

ance

rso

vera

llby

12%

inth

isBW

HSc

ohor

t.Ag

eat

first

child

’sbi

rthm

ayb

ean

impo

rtant

facto

rto

inclu

dein

ap

redi

ction

mod

elfo

rblac

kwo

men

,as

inth

eGa

ilm

odel

forw

hite

wom

en.T

hed

iscrim

inato

ryp

ower

oft

heC

ARE

mod

elin

blac

kwom

enw

asm

odes

t.Di

scrim

inati

onw

asw

orse

forE

R−b

reas

tcan

cer,

indi

catin

ga

need

tod

evelo

pa

bette

rrisk

mod

elfo

rwom

enw

ithth

isbr

east

canc

ersu

btyp

e.


47

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

5.P

etrac

ciE,

Dec

arli

A,

Scha

irerC

,eta

l.Ri

sk

Facto

rMod

ifica

tion

and

Proj

ectio

nso

fAb

solu

teBr

east

Canc

erR

isk.J

Na

tlCa

ncer

Inst

2011

;103

:1–1

2

ITALY

Coho

rt

Objec

tive:

Toes

timate

thee

ffects

ofc

hang

esin

mod

ifiab

leris

kfac

tors

on

thea

bsol

uter

isko

fbre

astc

ance

r.

Meth

odA

mod

elwa

sde

velo

ped

top

redi

ctth

eab

solu

teris

kof

br

east

canc

eru

sing

data

from

am

ultic

entre

cas

e–co

ntro

lstu

dyo

fwom

enin

Italy

(25

69c

ase

patie

nts

and

2588

co

ntro

lsub

jects

studi

edfr

omJ

une

1,1

991,

toA

pril

1,

1994

invo

lving

six

regi

ons)

and

incid

ence

and

mor

tality

da

tafro

mt

heF

lore

nce

Regi

stries

.Da

tafro

mt

hec

ase

cont

rols

tudy

wer

eus

edto

sele

ctth

eno

n-m

odifi

able

and

mod

ifiab

leris

kfac

tors

and

toe

stim

ateth

ere

lative

risk

s.Th

em

odel

inclu

ded

five

non-

mod

ifiab

le r

isk

fact

ors

(repr

oduc

tive

char

acter

istics

,ed

ucati

on,

occu

patio

nal

activ

ity,

family

hist

ory,

and

biop

syh

istor

y)a

ndt

hree

m

odifi

able

risk

facto

rs(

alcoh

olc

onsu

mpt

ion,

leis

ure

phys

icala

ctivit

y,an

dbo

dym

assi

ndex

).

Them

odel

wasv

alida

tedu

sing

inde

pend

entd

atafr

omth

eIta

lian

coho

rt,F

lore

nce-

EPIC

coh

orts

tudy

from

199

8to

20

04.

II-2

Mod

elde

velo

pmen

t:Ca

se–c

ontro

lstu

dyin

clude

d25

69

case

s(fem

alep

atien

tswi

thb

reas

tca

ncer,

age

d23

to

74y

ears

,m

edian

age:

55ye

ars)

;

cont

rols

ubjec

ts(n

=2

588)

wer

ewo

men

age

d20

to

74y

ears

(m

edian

age

:56

yea

rs)

with

out

brea

stca

ncer

Mod

elva

lidati

on:

TheF

lore

nce-

EPIC

coho

rtin

clude

d10

,083

wom

enag

ed3

5–64

year

s.

--

-Fi

nal r

elat

ive

risk

mod

el:

Fina

lrela

tiver

iskm

odel:

Valid

atio

n•

Calib

ratio

n:

Over

all:

Pre

dicte

dbr

east

canc

erc

ases

=2

25.7

Obse

rved

inva

sive

brea

stca

ncer

cas

es

=20

6

E/

Ora

tio=

1.1

0(9

5%C

I=0

.96

to1

.26,

p=0

..19

)

Othe

rvar

iables

:

T

hem

odel

show

edo

ver-p

redi

ction

ofb

reas

tcan

cerr

isks

forw

omen

age

d60

ye

arso

rmor

e,wo

men

aged

30

orm

orea

tfirs

tlive

birt

h,an

dwo

men

with

12

year

sorm

oreo

fedu

catio

n.

•Di

scrim

inati

onC

onco

rdan

ce(c

)-stat

istic

=0.

62(9

5%C

I=0

.55

to0

.69)

forw

omen

youn

ger

than

50

year

s(

c)-s

tatist

ic=

0.57

(95%

CI=

0.5

2to

0.6

1)fo

rwom

ena

ged

50y

ears

and

ol

der

Abso

lute

risk

redu

ctio

n

Mea

nab

solu

teris

kre

ducti

ons

(ARR

)=0

.6%

(95%

CI=

0.3

to1

.0%

)and

1.

4%(9

5%C

I=0

.7to

2.0

%)f

or4

5-ye

ar-o

ldw

omen

(fo

r10

and

20ye

ars

proj

ectio

n)

ARR

=0.9

%(9

5%C

I=0

.5%

to1

.3%

)and

1.6

%(9

5%C

I=0

.9%

to2

.3%

)fo

r65-

year

-old

wom

en(f

or1

0an

d20

year

spro

jectio

n)

Estim

ated

mea

nab

solu

teris

ksa

ndm

ean

abso

lute

risk

redu

ction

s w

ere

larg

er in

wom

en w

ith a

fam

ily h

isto

ry o

f bre

ast c

ance

r

ARR

=1.

3%(9

5%C

I=0

.7to

2.1

%)a

nd2

.7%

(95%

CI=

1.4

to4

.2%

)for

45

-yea

r-old

wom

en(1

0an

d20

year

spro

jectio

n)

ARR

=1.9

%(9

5%C

I=1

.1%

to2

.9%

)and

3.2

%(9

5%C

I=1

.8%

to4

.8%

)fo

r65-

year

-old

wom

en(1

0an

d20

year

spro

jectio

n)

Auth

orco

nclu

sion:

The

new

abso

lute

risk

pred

ictio

nm

odel

for

inva

sive

brea

stca

ncer

was

well

ca

libra

tedo

vera

llbu

tove

resti

mate

dth

eab

solu

teris

kof

bre

astc

ance

rin

som

esu

bgro

ups

such

as

wom

ena

ged

60y

ears

oro

lder,

and

wom

enw

hose

firs

tlive

bi

rtho

ccur

red

atag

e30

year

sorl

ater.

Thed

iscrim

inato

ryac

cura

cyw

asm

odes

t.Th

eab

solu

teris

kre

ducti

on

inclu

ding

ex

posu

re

mod

ifica

tions

wa

sne

arly

prop

ortio

nalt

oth

erisk

with

outi

nclu

ding

thes

efac

tors

and

incr

ease

dwi

thag

eand

ris

kpro

jectio

ntim

e.Th

ese

data

give

per

spec

tive

onth

epo

tentia

lred

uctio

nsin

abs

olut

ebr

east

canc

er

risk

from

pre

vent

ative

stra

tegies

bas

edo

nlif

estyl

ech

ange

s.Th

em

ethod

sar

eals

ous

efulf

orca

lculat

ing

sam

ples

izesr

equi

red

fort

rials

tote

stlif

estyl

eint

erve

ntio

ns.


48

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

6.V

acek

PM

,Ske

lly

JM,G

elleB

M.B

reas

tca

ncer

risk

asse

ssm

ent

inw

omen

aged

70

and

olde

r.Br

east

Canc

erR

esTr

eat2

011.

13

0:29

1–29

9.

VERM

ONT

&NE

W

HAM

PSHI

RE

Pros

pecti

veco

hort

study

Objec

tive:

Tod

eterm

ine

the

mod

elsu

sefu

lnes

sas

risk

as

sess

men

tto

ols

for

wom

ena

ged

70a

ndo

lder,

(th

eor

igin

alGa

ilm

odel,

the

Tice

mod

ifica

tion

oft

heG

ail

mod

el, t

he B

arlo

w M

odel

& th

e Ve

rmon

t m

odel)

, to

ex

amin

ethe

irpr

edict

ion

abili

tyto

diag

nose

bre

astc

ance

ran

dto

com

pare

mod

elpe

rform

ance

s

Meth

od

This

study

util

ized

data

from

theV

erm

ontB

reas

tCan

cer

Surv

eillan

ceS

ystem

(VB

CSS)

to

asse

mbl

ea

coho

rt,

obtai

nris

kfac

tor

info

rmati

ona

ndi

dent

ifys

ubse

quen

tca

seso

fbre

astc

ance

r.In

vasiv

ean

din

situ

bre

ast

canc

ers

diag

nose

dbe

fore

Ja

nuar

y1,2

010

were

iden

tified

usin

gbo

thth

epath

olog

yin

form

ation

inth

eVBC

SSan

dth

ediag

nosis

code

sin

the

Med

icare

&M

edica

idS

ervic

escl

aimsd

ata.

Risk

sco

reth

enc

ompu

tedb

ased

on

each

oft

hefo

urri

sk

mod

els.

II-2

Coho

rt of

19,

779

Verm

ont

wom

en

aged

70

an

dol

der

who

had

am

amm

ogra

m

in

the

Verm

ont

Brea

stCa

ncer

Su

rveil

lance

Sys

temb

etwee

n19

96

and

2001

Inclu

sion

- No

tpre

vious

lydi

agno

sed

with

br

east

canc

er,d

idn

otd

eclin

eti

uset

heir

data

Exclu

ded

- Pr

evale

nt

canc

ers,

wom

en

diag

nose

dwi

thca

ncer

Majo

rity

(97.

7%)

were

wh

ite,

(54.

6%)a

ged

betw

een

70-7

4

--

-

Incid

ence

ofb

reas

tcan

cer

821

deve

lope

dbr

east

canc

er(5

.0%

)

Risk

scor

eand

bre

astc

ance

rrisk

The

risk

scor

esc

ompu

tedf

rom

eac

hof

the

mod

elsw

ere

signi

fican

tly

asso

ciated

with

bre

astc

ance

rrisk

inth

eCox

regr

essio

nan

alyse

s(p<

0.00

1).

Howe

ver,

ther

egre

ssio

nco

efficie

nts(

ß’s)w

erea

llsig

nific

antly

lowe

rtha

non

e(

ß=

0.31

for

the

Gail

mod

el,ß

=0

.26

for

the

Tice

mod

el,ß

=0

.50

for

the

Barlo

wm

odel

and

ß=0

.54

for

the

Verm

ontm

odel)

indi

catin

gth

atth

eob

serv

edin

crea

ses

inr

elativ

eris

kpe

run

itof

the

risk

scor

ewe

re m

uch

low

erth

anp

redi

cted

byth

emod

els.

Disc

rimin

ation

C-sta

tistic

s=0

.54

(95%

CI=

0.5

2–0.

56)f

orth

eGail

mod

el,

C-sta

tistic

s=

0.54

(95

%C

I=0

.51–

0.56

)fo

rth

eTi

cem

odifi

catio

nof

the

Gail

mod

el,

C-sta

tistic

s=0

.55

(95%

CI=

0.5

3–0.

58)f

ora

mod

elde

velo

ped

byB

arlo

w

C-sta

tistic

s=0

.55

(95%

CI=

0.5

3–0.

58)f

ora

Verm

ontm

odel

Auth

orco

nclu

sion:

Th

em

odels

are

not

use

fulf

ora

sses

sing

risk

inw

omen

age

d70

and

old

er.A

ge-

relat

edat

tenua

tion

ofth

eeffe

ctso

fsom

erisk

facto

rsm

akes

thep

redi

ction

ofb

reas

tca

ncer

ino

lder

wom

enp

artic

ular

lydi

fficu

lt.N

ewri

skfa

ctors

and

bio

mar

kers

for

brea

stca

ncer

are

nee

ded

asw

ella

ssta

tistic

alm

odels

that

allow

their

effe

ctsto

va

ryw

ithag

e.

7.S

chon

feld

SJ,P

ee

D,G

reen

leeR

T,et

al.

Effec

tofC

hang

ing

Brea

stCa

ncer

In

ciden

ceR

ateso

nth

eCa

libra

tion

of th

e Gail

M

odel.

JCl

inO

ncol

20

10.2

8:24

11-2

417.

US

Pros

pecti

veco

hort

Objec

tive:

Toe

valu

atet

heG

ailm

odel

calib

ratio

nin

two

rec

ent

coho

rts(

Natio

nal

Insti

tutes

of

Healt

hAA

RPD

ieta

nd

Healt

hSt

udy

(NIH

-AAR

P,19

95

to

2003

),an

dth

ePr

ostat

e,Lu

ng,C

olor

ectal

and

Ovar

ianC

ance

rScr

eeni

ng

Trial

(PL

CO,

1993

to

2006

),fo

llowi

ngc

hang

esi

nUS

br

east

canc

erin

ciden

ced

urin

gth

e199

0s

Meth

odGa

ilm

odel

uses

bre

ast

canc

eri

ncid

ence

rate

san

dco

mpe

ting

mor

tality

ra

tes

from

th

eSu

rveil

lance

,Ep

idem

iolo

gya

ndE

ndR

esul

ts(S

EER)

pro

gram

(19

83

to1

987)

,whi

leth

isup

dated

mod

elus

edS

EER

incid

ence

an

dm

ortal

ityr

ates

from

per

iod

corre

spon

ding

to

the

coho

rts,1

995-

2003

.Can

cera

scer

tainm

entd

onet

hrou

gh

linka

gew

iths

tate

canc

erre

gistr

ies(N

IH-A

ARP)

,ann

ual

mail

ed

ques

tionn

aires

to

pa

rticip

ants

and

med

ical

reco

rds

confi

rmati

on(

PLCO

),re

ports

from

phy

sician

s,De

athIn

dex

and

canc

erre

gistr

ies.R

iskfa

ctors

inclu

ded

inth

eGa

ilm

odelo

btain

edfr

omb

aseli

neq

uesti

onna

ires.

Calib

ratio

nof

upd

ated

mod

elev

aluate

dus

ing

com

bine

dGa

ilm

odel

RRw

ithS

EER

brea

stca

ncer

inc

iden

ce

rates

1995

to2

003.

II-2

Wom

enf

rom

NIH

-AAR

P(a

lar

ge

pros

pecti

ve

study

of

20

0,00

0wo

men

ag

e50

to

71

ye

ars,

from

199

5to

199

6)a

ndP

LCO

(a

mul

ticen

ter

scre

enin

gtri

alin

volvi

ng7

7,50

0wo

men

,betw

een

1993

and

2001

;

Age

55t

o74

yea

rsa

tba

selin

ein

clude

dwh

ite,

post-

men

opau

sal

wom

en

with

kn

own

parit

y, no

hi

story

ofi

nsit

uor

inva

siveb

reas

tca

ncer,

and

age

you

nger

than

90

year

satt

hest

arto

ffol

low-

up

--

-

Incid

ence

ofb

reas

tcan

cer

5,66

5de

velo

ped

brea

stca

ncer

from

181

,979

wom

en(N

IH-A

ARP)

2,22

3de

velo

ped

brea

stca

ncer

from

64,

868

wom

en(P

LCO)

Over

alla

gea

djus

tedin

vasiv

ebr

east

canc

erin

ciden

cera

tes(S

EER

1995

to

2003

)=3

89.9

9pe

r100

,000

per

son

year

s;(3

43.0

per

100

,000

per

son

year

sin

SEE

R 19

83 to

198

7)

Calib

ratio

nTh

eGail

mod

elsig

nific

antly

und

erpr

edict

edth

enum

bero

finv

asive

bre

astc

ance

rs:

-E/O

ratio

=0

.87

(95%

CI,

0.85

to0

.89)

inN

IH-A

ARP

(und

erpr

edict

ion

by1

3%),

-E/

Ora

tio=

0.8

6(9

5%C

I,0.

82to

0.9

0)in

PLC

O(u

nder

pred

ictio

nby

14%

),

Over

all,t

heu

pdate

dm

odel

(SEE

R19

95to

200

3)w

asw

ellca

libra

ted:

E

/Ora

tio=

1.0

3(9

5%C

I,1.

00to

1.0

5)in

NIH

-AAR

Pan

dE

/Ora

tioo

f1.0

1(9

5%C

I:0.

97to

1.0

6)in

PLC

O.

T

heG

ailm

odel

was

well

calib

rated

inP

LCO

when

the

pred

ictio

npe

riod

was

restr

icted

to2

003

to2

006

(E/O

ratio

=1.

00;9

5%CI

,0.9

4to

1.0

8).

Disc

rimin

ation

D

iscrim

inato

rya

ccur

acy

was

unch

ange

dby

the

reca

libra

tion;

the

over

allA

UCs

fort

heu

pdate

dm

odel

were

0.5

8in

NIH

-AAR

Pan

d0.

59in

PLC

O.

Auth

orco

nclu

sion:

The

calib

ratio

nof

risk

pre

dicti

onm

odels

is

sens

itive

to

trend

sin

und

erlyi

ng

popu

latio

nra

tes.

This

study

hig

hlig

hts

the

impo

rtanc

eof

usin

gap

prop

riate

base

line

rates

ina

bsol

ute

risk

mod

elsa

ndth

eim

porta

nce

ofp

redi

ction

mod

els

calib

ratio

nto

ensu

reth

eiru

sefu

lnes

sfor

clin

icald

ecisi

onm

akin

g.


49

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

8.C

hayW

Y,On

gW

S,

Tan

PH,e

tal.V

alida

tion

of th

e Ga

il m

odel

for

pred

ictin

gin

divid

ualb

reas

tca

ncer

risk

ina

pros

pecti

vena

tionw

ides

tudy

of

28,1

04S

inga

pore

wo

men

.Bre

astC

ance

rRe

sear

ch2

012,

14:

R19

Valid

ation

stud

y(pr

ospe

ctive

coho

rt)

Objec

tive:

to

evalu

ate

the

perfo

rman

ce

of

the

Gail

Mod

el(GM

)as

an

ap

prop

riate

brea

stca

ncer

ris

kas

sess

men

ttoo

lin

theA

sian

popu

latio

n.

Meth

od

The

study

pop

ulati

onc

onsis

tedo

f28,

104

wom

ena

ged

50to

64

year

swh

opa

rticip

ated

inth

eSi

ngap

ore

Brea

stCa

ncer

Scr

eeni

ngP

rojec

t(S

BSCP

)an

ddi

dno

tha

ve

brea

stca

ncer

dete

cted

durin

gsc

reen

ing.

Ca

ncer

asc

ertai

nmen

twas

thro

ugh

the

Sing

apor

eCa

ncer

Re

gistr

yan

dth

eNa

tiona

lDea

thR

egist

er.T

oev

aluate

the

perfo

rman

ceo

fthe

GM

,the

exp

ected

num

bero

finv

asive

br

east

canc

erc

ases

wer

eco

mpa

red

ton

umbe

rofa

ctual

case

sobs

erve

dwi

thin

5-y

eara

nd1

0-ye

arfo

llow-

up.

II-2

n=2

8,10

4wo

men

age

d50

to6

4ye

ars

from

Si

ngap

ore

Brea

stCa

ncer

Sc

reen

ing

Proj

ect

(SBC

SP),

apop

ulati

on-b

ased

mam

mog

raph

ysc

reen

ing

proj

ect

amon

gfem

aleS

inga

pore

ans

cond

ucted

be

twee

nOc

tobe

r19

94

and

Febr

uary

199

7.

Exclu

ded:

wo

men

wi

th

brea

stca

ncer

oro

ther

can

cers

,pre

gnan

tor

had

mam

mog

raph

yor

bre

ast

biop

sy1

2m

onth

sprio

rstu

dy

--

-

Incid

ence

ofb

reas

tcan

cer

144

and

409

inva

sive

brea

stca

ncer

swe

red

iagno

sed

with

in5

yea

rsa

nd1

0ye

ars

from

scre

enin

g,re

spec

tively

Calib

ratio

n-5

year

from

scre

enin

g-

Ove

rall:

Pred

icted

inva

siveb

reas

tcan

cerc

ases

=3

62ca

ses

Obse

rved

inva

siveb

reas

tcan

cerc

ases

=1

44ca

ses

E/O

ratio

=2

.51

(95%

CI=

2.1

4to

2.9

6)

The

GMo

ver-e

stim

ated

brea

stca

ncer

risk

acr

oss

alla

geg

roup

s,wi

th

theh

ighe

stdi

scre

panc

yam

ong

olde

rwom

enag

ed6

0-6

4ye

ars(

E/O

=3.

53,9

5%C

I=2

.57-

4.85

)1

0ye

arfr

omsc

reen

ing

Over

all:

Pred

icted

inva

siveb

reas

tcan

cerc

ases

=7

58ca

ses

Obse

rved

inva

siveb

reas

tcan

cerc

ases

=4

09ca

ses

E/O

ratio

=1

.85

(95%

CI=

1.6

8to

2.0

4)

Over

-pre

dicti

ono

fthe

num

bero

fbre

astc

ance

rsb

yGM

was

hig

herf

or

wom

enw

how

ere

60to

64

year

sol

d(E

/Ora

tio=

2.5

4,9

5%C

I=2

.57

to4

.85)

Tren

dso

fE/O

rati

oby

age

gro

upa

ndp

redi

cted

risk

quin

tile

grou

pwh

ich

were

bas

edo

n10

-yea

rpr

edict

ion

were

bro

adly

simila

rto

thos

eba

sed

on

5-ye

arp

redi

ction

.

Auth

orco

nclu

sion:

Th

isstu

dyva

lidate

sthe

Gail

mod

elris

kfac

tors

indi

vidua

llyb

utit

ove

r-pre

dicts

the

risk

ofin

vasiv

ebr

east

canc

erin

the

setti

ngo

fad

evelo

ped

Asian

cou

ntry

,with

the

large

stdi

ffere

nces

een

ino

lder

wom

enag

edb

etwee

n60

and

64ye

arso

ld.

Cons

eque

ntly,

it

has

beco

me

impe

rativ

eto

ev

aluate

lo

cal

brea

stca

ncer

ep

idem

iolo

gyin

ord

erto

vali

date

exist

ing

mod

elso

fbre

astc

ance

rris

k.Fu

ture

wo

rks

houl

dfo

cus

ont

hed

evelo

pmen

tof

app

ropr

iately

cali

brate

dm

odels

for

be

tterp

redi

ction

ofr

isk,w

hich

wou

ldb

enefi

tind

ividu

alco

unse

lling

and

can

cer

prev

entio

nre

sear

ch.


50

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

9.P

feiffe

rRM

,Par

kY,

Kreim

erA

R,et

al.R

isk

Pred

ictio

nfo

rBre

ast,

Endo

metr

ial, a

nd

Ovar

ianCa

ncer

in W

hite

Wom

enA

ged

50y

or

Olde

r:De

rivati

onan

d Va

lidati

on fr

om

Popu

latio

n-Ba

sed

Coho

rtSt

udies

.PL

oSM

ed2

013.

10

(7):

e100

1492

.do

i:10.

1371

/jour

nal.

pmed

.100

1492

US

Coho

rtstu

dy

Objec

tive:

Tod

evelo

pa

mod

elin

pre

dicti

ngr

isko

fcan

cers

that

shar

em

any

horm

onal

and

epid

emio

logi

cal

riskf

acto

rs;b

reas

t,en

dom

etrial

and

ovar

ianca

ncer

Meth

odAb

solu

teris

kpre

dicti

onm

odel

wasd

evelo

ped

forb

reas

t,en

dom

etrial

,an

dov

arian

can

cer

byc

ombi

ning

data

fro

mtw

olar

gep

rosp

ectiv

eco

horts

;the

Nati

onal

Canc

er

Insti

tute

(the

Pros

tate,

Lung

,Co

lore

ctal,

and

Ovar

ianC

ance

rSc

reen

ing

Trial

(PL

CO)

and

the

Natio

nal

Insti

tutes

of

Healt

h–AA

RPD

ietan

dHe

alth

Stud

y(N

IH-A

ARP)

);an

dfro

ma

ge-s

pecifi

cUS

pop

ulati

onin

ciden

cea

ndc

ompe

ting

mor

tality

rates

in

the

NCI’s

Sur

veill

ance

,Epi

dem

iolo

gy,a

ndE

ndR

esul

tsPr

ogra

m(S

EER)

.(h

ttp:

//se

er.c

ance

r.go

v/ab

out/

over

view

.htm

l).

Parti

cipan

tsco

mpl

eted

self

adm

inist

ered

que

stion

naire

at

entry

.Bre

astc

ance

rm

odel

inclu

des

MHT

use

,oth

er

MHT

use

,age

atfir

stliv

ebi

rth,m

enop

ausa

lstat

us,a

ge

atm

enop

ause

,fam

ilyh

istor

y,be

nign

bre

astd

isas/

biop

sy,

alcoh

ol,B

MI

Canc

ers

were

asc

ertai

ned

viaa

nnua

lstu

dyu

pdate

san

dde

athc

ertifi

cates

,an

dve

rified

via

revie

wof

med

ical

reco

rds

(PLC

O),

thro

ugh

linka

ge

with

sta

teca

ncer

re

gistr

ies(N

IH-A

ARP)

.

Allm

odels

wer

eva

lidate

dus

ing

inde

pend

entd

atafr

om

theN

urse

s’He

alth

Stud

y(NH

S).

II-2

PLCO

en

rolle

d78

,232

wo

men

,ag

ed

55

to

74

yat

base

line

(betw

een

Nove

mbe

r19

93

and

June

200

1)

NIH-

AARP

inclu

ded

567,

169

men

an

dwo

men

age

d50

to7

1ye

ars

(199

5–19

96)

Restr

ictio

n:N

on-H

ispan

ic,w

hite

wom

enw

hoc

ompl

eted

base

line

ques

tionn

aire,

had

follo

w-up

in

form

ation

,and

had

no

pers

onal

histo

ryo

fthe

can

cero

fint

eres

tat

base

line,

Fina

l stu

dy p

opul

atio

n in

clude

d19

1,60

4(N

IH-A

ARP)

and

64,4

40(P

LCO)

wom

enfo

rbre

ast

canc

eran

alysis

Form

odel

valid

ation

:Fi

nal v

alida

tion

inclu

ded

57,9

06

wom

enfr

omth

eNHS

,age

d30

–55

(fort

heb

reas

tcan

cerm

odel)

Fina

lrela

tiver

iskm

odel

(bre

astc

ance

r)

Varia

bles

inc

lude

din

the

fina

lRR

mod

el:B

MI,

estro

gen

and

prog

estin

M

enop

ause

Hor

mon

eTh

erap

yus

e,ot

herM

HTu

se,p

arity

,age

atfi

rstb

irth,

pr

emen

opau

sal,

age

atm

enop

ause

,ben

ign

brea

stdi

seas

es,f

amily

hist

ory

of

brea

stor

ova

rian

canc

er,an

dalc

ohol

cons

umpt

ion

Brea

stca

ncer

risk

pro

jectio

n-

Abso

lute

riske

stim

ates=

1.5

7%to

21.

78%

(10

year

pro

jectio

n)-

Abso

lute

riske

stim

ates=

3.6

4%to

35.

11%

(20

year

pro

jectio

n)

Valid

ation

Calib

ratio

n•

Over

all-

Incid

ence

ofb

reas

tcan

cero

bser

ved

=2,9

34-

Incid

ence

ofb

reas

tcan

cerp

redi

cted

=2,9

30-

E/O

ratio

=1

.00

(95%

CI:

0.96

to1.

04).

- Th

em

odel

signi

fican

tlyu

nder

estim

ated

the

num

ber

ofb

reas

tca

ncer

sin

pr

emen

opau

salw

omen

;E/O

ratio

=0

.73

(95%

CI:

0.65

to0

.83)

.-

E/O

ratio

swer

enot

stati

stica

llysi

gnifi

cant

lyfo

roth

erva

riabl

es

Disc

rimin

ation

Over

allA

UC(d

iscrim

inato

ryp

ower

)in

the

NHS

coho

rt=

0.58

(95%

CI:

0.57

to

0.5

9).

Com

pare

dto

BCR

AT;

BCRA

Tpr

edict

ed2

,947

cas

esr

esul

ting

inE

/O

1.0(

95%

CI0

.97

to1

.04)

Auth

orco

nclu

sion:

Thes

em

odels

pre

dict

abso

lute

risks

forb

reas

t,en

dom

etrial

,and

ova

rian

canc

ers

from

eas

ilyo

btain

able

risk

facto

rsa

mon

gwh

ite,n

on-H

ispan

icwo

men

age

d50

+ye

ars.

The

mod

elsm

aya

ssist

inc

linica

ldec

ision

-mak

ing

relat

edto

the

risks

of

thes

eca

ncer

san

dm

ight

impr

ove

the

abili

tyto

iden

tify

poten

tialp

artic

ipan

tsfo

rre

sear

chst

udies

.Lim

itatio

nsar

ethe

mod

estd

iscrim

inato

ryab

ility

ofth

ebre

asta

nd

ovar

ianm

odels

,and

that

thes

emod

elsm

ayn

otg

ener

alize

tow

omen

ofo

ther

race

s.


51

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

10.B

aneg

asM

P,Ga

ilM

H,L

aCro

ixA,

et

al.E

valu

ating

bre

ast

canc

erri

skp

rojec

tions

fo

rHisp

anic

wom

en.

Brea

stCa

ncer

Res

Tr

eat.

2012

Feb

ruar

y;

132(

1):.

doi

:10.

1007

/s1

0549

-011

-190

0-9.

US

Valid

ation

stud

y(co

hort)

Objec

tive:

Toe

xam

ine

the

perfo

rman

ceo

fBre

astC

ance

rRi

skA

sses

smen

tToo

l(BC

RAT)

inH

ispan

icwo

men

Meth

odBC

RAT

com

bine

s19

90-1

996

brea

stca

ncer

inc

iden

ce

forH

ispan

icwo

men

with

relat

iveri

sko

frisk

facto

rsfr

om

NHW

.In

th

isstu

dy,

the

relat

ive

risks

,ca

libra

tion

and

disc

rimin

atory

acc

urac

yof

BCR

ATr

iskp

rojec

tions

for

6,

353

Hisp

anic

were

com

pare

dto

128

,976

Non

-Hisp

anic

Whi

te(N

HW)p

ostm

enop

ausa

lpar

ticip

ants

aged

50–7

9in

the

Wom

en’s

Healt

hIn

itiati

ve(W

HI),

alar

gelo

ngitu

dina

lstu

dyin

volvi

ngH

ispan

ican

dNH

W.

BCRA

Tris

kfac

tors

(ag

e,ag

eat

first

live

birth

,age

at

men

arch

e,nu

mbe

rof

firs

t-deg

ree

relat

ives

with

bre

ast

canc

er,n

umbe

rof

bre

ast

biop

sies

and

pres

ence

of

atypi

calh

yper

plas

iaon

ap

revio

usb

reas

tbio

psy)

wer

eob

taine

dfro

mth

een

rollm

entq

uesti

onna

ire.A

llre

porte

din

vasiv

ebr

east

canc

ers

were

adj

udica

tedl

ocall

yan

dce

ntra

lly.C

alibr

ation

for

an

‘upda

tedB

CRAT

’wa

sre

-ev

aluate

dth

atco

mbi

ned

BCRA

TRR

with

199

3–20

07

SEER

bre

ast

canc

eri

ncid

ence

ssim

ultan

eous

with

the

W

HI.

II-2

6,35

3Hi

span

ican

d12

8,97

6NH

W

postm

enop

ausa

lpar

ticip

ants

aged

50

–79

with

outa

hist

ory

ofb

reas

tca

ncer

orm

astec

tom

yin

WHI

--

-

Valid

atio

n of

BCR

AT•

Calib

ratio

n T

hem

odel

unde

resti

mate

dth

enu

mbe

rofb

reas

tcan

cerd

iagno

ses

amon

gHi

span

icsb

y18%

(O/E

=1

.18,

95%

CI=

0.9

9to

1.4

0;p

=0.

06)

T

hem

odel

also

unde

resti

mate

dth

enu

mbe

rofb

reas

tcan

cerd

iagno

ses

by

18%

forN

HWw

omen

(O/E

=1

.18,

95%

CI=

1.1

4to

1.2

1;p

<0.

001)

.•

Conc

orda

nces

tatist

ic(A

UC)o

fBCR

AT=

0.6

3[9

5%CI

:0.5

8to

0.6

7]fo

rHisp

anic

=0.

58[9

5%CI

:0.5

6to

0.5

8]fo

rNHW

wom

enin

theW

HI(p

=0.

03)

Valid

atio

n of

upd

ated

mod

el

Upda

ting

theB

CRAT

impr

oved

calib

ratio

nfo

rHisp

anic

wom

en(O

/E=

1.0

8,p

=0.

4)

and

NHW

wom

en(O

/E=

0.9

8,p

=0.

2).

C

ompa

rison

of R

R (B

CRAT

vs

WHI

)•

ForH

ispan

icwo

men

,rela

tive

risks

forn

umbe

rofb

reas

tbio

psies

(RR=

1.7

1vs

.1.2

7,p

=0.

03)a

nda

gea

tfirs

tbirt

h(R

R=

0.97

vs.

1.24

,p=

0.02

)wer

esig

nific

antly

diff

eren

tbetw

een

theW

HIan

dBC

RAT.

•Th

eon

lyRR

esti

mate

that

signi

fican

tlyd

iffer

entb

etwee

nNH

Wa

ndH

ispan

icwo

men

inth

eWHI

was

num

bero

fbre

astb

iops

ies(R

R=

1.27

vs1

.71,

p=

0.03

).

Auth

orc

onclu

sion:

Upd

ating

the

BCR

ATw

ithc

ontem

pora

neou

sbr

east

canc

er

incid

ence

rate

sim

prov

edc

alibr

ation

in

the

WHI

.Th

em

odes

tdi

scrim

inato

ry

accu

racy

oft

heB

CRAT

forH

ispan

icwo

men

mig

htb

eim

prov

edb

yus

ing

relat

ive

risks

ofr

iskfa

ctors

pecifi

cto

Hisp

anic

wom

en.F

urth

erd

evelo

pmen

tofa

mor

eco

mpr

ehen

sive

mod

elfo

rHi

span

icwo

men

,th

atco

nsid

ers

brea

stca

ncer

risk

fac

tors

dist

inct

toH

ispan

icwo

men

and

isva

lidate

din

alar

geco

hort

ofU

SHi

span

icwo

men

,isw

arra

nted

.


52

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

11.B

oggs

DA,

Ro

senb

erg

L,

Adam

s-Ca

mpb

ell

LL,a

ndP

almer

JR.

Pros

pecti

veA

ppro

ach

toB

reas

tCan

cerR

isk

Pred

ictio

nin

Afric

anA

mer

ican

Wom

en:T

heB

lack

Wom

en’s

Healt

hSt

udyM

odel.

JCl

in

Onco

l201

5.33

;p1

-7.D

OI:1

0.12

00/

JCO.

2014

.57.

2750

Unite

dSt

ates

Valid

ation

stud

y(pr

ospe

ctive

coho

rt)

Objec

tive:

Tod

evelo

pa

brea

stca

ncer

risk

pre

dicti

onm

odel

for

Afric

anA

mer

ican

wom

enu

sing

pros

pecti

ved

atafr

omth

eBW

HSin

follo

w-up

from

199

5to

200

5an

dto

vali

date

the

mod

elin

sub

sequ

entf

ollo

w-up

from

200

6to

201

0,

over

allan

dby

agea

ndE

Rsta

tus

Meth

od:

The

BWHS

is

ano

ngoi

ngf

ollo

w-up

stu

dyo

fAf

rican

Am

erica

nwo

men

esta

blish

edin

199

5in

volvi

ng5

9,00

0Af

rican

Am

erica

nwo

men

from

acr

oss

the

US.A

bre

ast

canc

err

iskm

odel

for

the

wom

enw

asd

evelo

ped

usin

gre

lative

risk

s(R

R)o

fin

vasiv

ebr

east

canc

erd

erive

dfro

m1

0ye

ars

off

ollo

w-up

(19

95t

o20

05)

ofB

WHS

pa

rticip

ants

age

30t

o69

yea

rsa

tba

selin

eby

Cox

pr

opor

tiona

lhaz

ards

mod

els.

Abso

lute

riskw

ases

timate

dus

ing

RRsa

ndat

tribu

table

risks

from

theB

WHS

mod

el,ag

e-sp

ecifi

cSEE

Rbr

east

canc

erra

tesfo

rAfri

can

Amer

ican

wom

en(1

994

to19

98),

and

ages

pecifi

ccom

petin

gm

ortal

ityra

tesfo

rAfri

can

Amer

ican

wom

en(1

996

to2

000)

.Us

ing

the

subs

eque

nt5

yea

rso

ffol

low-

upd

ata(2

006

to

2010

),ca

libra

tion

asth

era

tioo

fexp

ected

too

bser

ved

num

ber

ofb

reas

tcan

cers

and

disc

rimin

atory

acc

urac

yus

ing

thec

onco

rdan

cest

atisti

cwer

eeva

luate

d.

Base

lineq

uesti

onna

ireo

nBW

HSri

skfa

ctorw

ere

colle

cted

(age

atm

enar

che,

heig

ht,w

eight

atag

e18

year

s,wa

istan

dhi

pcir

cum

feren

ce,e

duca

tiona

latt

ainm

ent,

family

hist

oryo

fbre

astc

ance

r,pa

rity,

agea

tfir

stbi

rth,d

urati

ono

fbre

astfe

edin

g,o

ralc

ontra

cept

ive

use,

agea

tmen

opau

se,t

ypeo

fmen

opau

se,m

enop

ausa

lho

rmon

euse

,pre

vious

diag

nosis

ofb

enig

nbr

east

dise

ase

Incid

entd

iagno

seso

fbre

astc

ance

rwer

easc

ertai

ned

thro

ugh

self-

repo

rton

bien

nial

follo

w-up

que

stion

naire

san

d,fo

rnon

-resp

onde

nts,

thro

ugh

linka

gew

ithst

ate

canc

erre

gistr

ies.B

reas

tcan

cerd

eath

swer

eide

ntifi

ed

thro

ugh

linka

gew

ithth

eNati

onal

Death

Inde

x.

II-2

55,8

79A

frica

nAm

erica

nwo

men

fro

mt

heB

WHS

coh

ort

aged

30

to6

9ye

ars

atba

selin

eor

who

re

ache

dag

e30

ye

ars

durin

gfo

llow-

up,h

adn

ohi

story

ofc

ance

rat

base

line,

and

had

nom

issin

gda

taon

varia

bles

inth

efina

lmod

el

Mea

nag

e:39

.9(1

996

coho

rt)a

nd

47.2

(200

6)

TheB

WHS

mod

elin

clude

dth

esef

acto

rs;

family

hist

ory,

prev

ious

bio

psy,

body

mas

sind

exat

age1

8ye

ars,

agea

tmen

arch

e,ag

eat

first

birth

,ora

lcon

trace

ptive

use

,bila

teral

ooph

orec

tom

y,es

troge

npl

us

prog

estin

use

,and

heig

ht(

facto

rass

ociat

edw

ithth

elar

gest

RR=

fam

ilyh

istor

yof

bre

astc

ance

rin

afirs

t-deg

reer

elativ

ediag

nose

dbe

fore

age5

0ye

ars(

RR=

2.24

;95

%C

I1.7

6to

2.8

4))

Incid

ence

ofb

reas

tcan

cer:

896

incid

enti

nvas

iveb

reas

tcan

cers

(199

5to

200

5)M

edian

agea

tdiag

nosis

:50

year

s

Valid

ation

:C

alibr

ation

O

vera

llE/

Ora

tio=

0.9

6(9

5%C

I0.8

8to

1.0

5)

s

igni

fican

tund

eres

timati

ono

frisk

:W

omen

with

out

afam

ilyh

istor

yof

bre

ast

canc

er(

E/O

ratio

,0.

90;9

5%

CI,0

.81

to0

.99)

,n

ullip

arou

swom

en(E

/Ora

tio=

0.83

;95%

CI0

.70

to0

.99)

,w

omen

who

use

dor

alco

ntra

cept

ives

with

inth

epr

evio

us1

0ye

ars

(E/O

ra

tio=

0.75

;95%

CI0

.63

to0

.89)

I

nth

esec

ond

lowe

stag

e-ad

juste

d

qui

ntile

ofr

isk(E

/Ora

tio=

0.7

5;9

5%C

I0.6

1to

0.9

1)

G

ood

agre

emen

tbe

twee

nth

epr

edict

eda

ndo

bser

ved

num

ber

ofb

reas

tca

ncer

sacr

ossc

atego

rieso

foth

erri

skfa

ctors

.

Disc

rimin

atory

accu

racy

A

gead

juste

dAU

C(o

vera

ll)=

0.5

9(9

5%C

I0.5

6to

0.6

1)A

UC=

0.6

2;9

5%C

I,0.

58to

0.6

5(w

omen

youn

gert

han

age5

0ye

ars)

A

UC=

0.5

6;9

5%C

I,0.

53to

0.5

9(w

omen

agem

oret

han

50ye

ars)

A

UC=

0.6

2(E

R-ne

gativ

ebre

astc

ance

r)A

UC=

0.5

8(E

R-po

sitive

bre

astc

ance

r)(p

=0.1

)P

ropo

rtion

ofw

omen

with

elev

ated

risk

ofb

reas

tcan

cer(

5-ye

arp

redi

cted

risko

f1.6

6%o

rgre

ater)

2

.8%

ofw

omen

youn

gert

han

50ye

arso

ld3

2.2%

ofw

omen

mor

etha

n50

year

sold

wer

eclas

sified

asb

eing

atele

vated

ris

kofi

nvas

iveb

reas

tcan

cer

Auth

orco

nclu

sion:

The

BWHS

mod

elwa

swe

llca

libra

tedo

vera

ll,a

ndth

epr

edict

ivea

bilit

ywa

sbe

stfo

ryou

nger

wom

en.T

hep

ropo

rtion

ofw

omen

pre

dicte

dto

mee

tthe

1.6

6%c

ut

poin

tcom

mon

lyus

edto

dete

rmin

eelig

ibili

tyfo

rbre

astc

ance

rpre

vent

ion

trials

was

gr

eatly

incr

ease

dre

lative

top

revio

usm

odels


53

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

12.A

noth

aisin

tawee

,Te

eraw

attan

anon

Y,

Wira

tkapu

nc,

etal

.De

velo

pmen

tand

Va

lidati

ono

faB

reas

tCa

ncer

Risk

Pre

dicti

onM

odel

forT

haiW

omen

:A

Cros

s-Se

ction

alSt

udy.

Asia

nPa

cJC

ance

rPr

ev.2

014.

15

(16)

,68

11-6

817

Cros

ssec

tiona

l

Objec

tive:

tod

evelo

pan

dva

lidate

ab

reas

tcan

cer

risk

pred

ictio

nm

odel

forT

haiw

omen

.

Meth

od:

•M

odel

deriv

ation

(usin

gda

taco

llecte

dat

Ram

athib

odi

hosp

ital)

•M

ultip

lelo

gisti

cre

gres

sion

was

appl

iedto

con

struc

tth

e mod

el•

Mod

elva

lidati

on(i

nter

nal)

:use

db

ootst

rap

with

200

re

petit

ions

•

Mod

elva

lidati

on

(exte

rnal)

us

ed

data

from

Sr

inag

arin

dan

dSo

ngkla

naga

rind

Hosp

itals

•Pe

rform

ance

wer

eas

sess

edw

hich

wer

eca

libra

tion

and

disc

rimin

ation

per

form

ance

susin

gth

eobs

erve

d/ex

pecte

dra

tioa

ndc

onco

rdan

ces

tatist

ic(C

-stat

istic)

,re

spec

tively

.

III17

,506

wom

en w

hom

und

erto

ok

mam

mog

raph

icsc

reen

ing

in

Ram

athib

odi

Hosp

ital

(mod

elde

rivati

on)

Wom

en

unde

rgoi

ng

mam

mog

raph

icsc

reen

ing

aged

18

year

sand

abov

ewer

eelig

ible.

Thos

ewith

hist

oryo

finv

asive

br

east

canc

er,d

uctal

carc

inom

ain

situ

oro

ther

canc

ersw

ere

exclu

ded

•M

odel

deriv

ation

inclu

ded

thes

erisk

facto

rs;

- ag

e,-

men

opau

sals

tatus

,-

body

mas

sind

exan

d-

useo

fora

lcon

trace

ptive

s

•M

odel

perfo

rman

ce:

-

O/E

ratio

=1

.0(9

5%CI

0.8

2to

1.2

1)(o

vera

ll)-

O/E

ratio

0.9

7(9

5%C

I0.6

8to

1.3

5)(E

xtern

alva

lidati

on)

- Ov

erall

c-sta

tistic

=0

.65

(95%

CI0

.59

to0

.70

- C-

statis

tic=

0.6

4(95

%C

I0.6

4to

0.6

5)In

terna

lvali

datio

n-

C-sta

tistic

=0

.61(

95%

CI0

.51

to0

.71)

Exte

rnal

valid

ation

•Ri

sksc

ore&

risk

stra

tifica

tion:

lo

w,lo

w-in

term

ediat

e,in

term

ediat

e-hi

gh,a

ndh

igh-

risk


54

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p(if

app

licab

le)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

1. K

ye S

Y, P

ark

K, P

ark

HG

et

al. P

sych

olog

ical

im

pact

of h

ealth

ris

k ap

prai

sal o

f K

orea

n w

omen

at

diff

eren

t lev

els

of b

reas

t can

cer

risk:

neg

lect

ed

aspe

ct o

f the

w

eb-b

ased

ca

ncer

risk

as

sess

men

t too

l. A

sian

Pac

ific

J C

ance

r Pre

v.

2012

;13:

437-

441

KO

RE

A

Ran

dom

ized

tria

l

Obj

ectiv

e:To

exp

lore

the

psyc

holo

gica

l effe

ct o

f he

alth

risk

app

rais

al u

sing

epi

dem

iolo

gica

l ris

k fa

ctor

pro

file

Met

hod:

P

artic

ipan

ts w

ere

recr

uite

d th

roug

h ad

verti

sem

ents

in th

e w

ebsi

tes

betw

een

Oct

ober

and

Nov

embe

r 200

9.60

elig

ible

w

omen

com

plet

ed a

bas

elin

e in

terv

iew

, th

en ra

ndom

ized

to e

ither

a n

umer

ical

HR

A

or th

e H

RA

usi

ng p

erso

nal r

isk

fact

or li

st.

HR

A w

as d

one

thro

ugh

tele

phon

e in

terv

iew

. A

fter o

ne m

onth

, the

par

ticip

ants

atte

nded

in

terv

entio

n se

ssio

ns w

hich

con

sist

of

prov

isio

n of

HR

A re

sults

, pre

- and

pos

t-H

RA

ass

essm

ent f

or p

sych

olog

ical

sta

tus,

an

d co

unse

lling

. V

aria

bles

mea

sure

d in

per

sona

l ris

k fa

ctor

; hei

ght,

wei

ght,

alco

hol,

vege

tabl

e co

nsum

ptio

n, p

ast h

isto

ry o

f ben

ign

brea

st

dise

ase,

men

stru

atio

n an

d O

CP

. R

isk

fact

ors

for n

umer

ical

HR

A u

sed

Gai

l m

odel

fact

ors.

Anx

iety

leve

l was

mea

sure

d us

ing

Spi

elbe

rger

Sta

te T

rait

Anx

iety

In

vent

ory

YZ

(Kor

ean

vers

ion)

.

n= 6

0 w

omen

aged

bet

wee

n 30

to 4

8 ye

ars

old

with

out p

rior

canc

er d

iagn

osis

med

ian

age

: 38

year

s

Info

rmat

ion

on

indi

vidu

aliz

ed

risk

for b

reas

t ca

ncer

No

info

rmat

ion

1 m

onth

•

Ave

rage

anx

iety

leve

l•

Mea

n(S

D) 4

4.7(

9.2)

for t

rait

(long

term

) anx

iety

( 42

.6(9

.2)

for s

tate

(tem

pora

ry) a

nxie

ty•

Pre

dict

or fo

r inc

reas

e in

anx

iety

- A

nxie

ty s

core

incr

ease

d am

ong

wom

en w

ho h

ad h

ighe

r ris

k st

atus

in ‘r

isk

scor

e’ g

roup

at f

ollo

w u

p co

mpa

red

to

base

line,

with

mea

n(S

D) a

t bas

elin

e an

d fo

llow

up

wer

e 42

.8(8

.5) a

nd 4

7.0(

9.6)

resp

ectiv

ely,

how

ever

it w

as n

ot

sign

ifica

nt-

Ris

k st

atus

was

the

inde

pend

ent p

redi

ctor

of i

ncre

ase

of

stat

e an

xiet

y af

ter h

ealth

risk

app

rais

al in

terv

entio

n, a

fter

adju

sted

for a

ge, e

duca

tion,

HR

A ty

pe, n

umer

acy,

pre-

HR

A s

tate

anx

iety

and

HR

A ty

pe.

- W

omen

who

had

hig

her r

isk

stat

us h

ad a

n od

d of

hav

ing

incr

ease

d an

xiet

y ab

out 5

tim

es g

reat

er th

an w

omen

who

ha

d lo

wer

risk

sta

tus

(OR

5.0

3, 9

5% C

I 1.5

4 to

16.

43,

p=0.

01)

Aut

hors

’ con

clus

ion:

C

omm

unic

atin

g th

e ris

k st

atus

by

indi

vidu

al h

ealth

risk

ap

prai

sal s

ervi

ce c

an in

duce

psy

chol

ogic

al s

eque

lae,

es

peci

ally

in w

omen

hav

ing

high

er ri

sk s

tatu

s. H

ospi

tals

, in

stitu

tes,

or m

edic

al s

choo

ls th

at a

re o

pera

ting

or p

lann

ing

to o

pera

te th

e on

line

heal

th ri

sk a

ppra

isal

ser

vice

sho

uld

take

si

de e

ffect

s su

ch a

s ps

ycho

logi

cal s

eque

lae

into

con

side

ratio

n.

Furth

er re

sear

ch o

n ca

ncer

risk

com

mun

icat

ion

stra

tegi

es fo

r H

RA

ser

vice

are

requ

ired

to e

xplo

re lo

ng te

rm o

utco

me

and

to

eval

uate

mor

e ps

ycho

logi

cal o

utco

me.

EVID

ENC

E TA

BLE

: S

AFE

TY

QU

ESTI

ON

: I

S H

EA

LTH

RIS

K A

SS

ES

SM

EN

T M

OD

ULE

FO

R B

RE

AS

T C

AN

CE

R O

R C

AN

CE

R R

ISK

PR

ED

ICTI

ON

MO

DE

L S

AFE

TO

DE

TEC

T W

OM

EN

WH

O D

EV

ELO

P B

RE

AS

T C

AN

CE

R?


55

Bibl

iogr

aphi

cci

tatio

nSt

udy

Type

/ M

etho

dsLE

Num

ber o

f pa

tient

s an

dpa

tient

ch

arac

teris

tics

Inte

rven

tion

Com

paris

onLe

ngth

of

follo

w u

p (if

ap

plic

able

)

Outc

ome

mea

sure

s/

Effe

ct s

ize

Gene

ral

com

men

ts

2. S

cher

er

LD, U

bel P

A,

McC

lure

J e

t al

. Bel

ief i

n nu

mbe

rs: w

hen

and

why

wom

en

disb

elie

ve

tailo

red

brea

st

canc

er ri

sk

stat

istic

s. P

atie

nt

educ

atio

n an

d co

unse

ling.

20

13;9

2:25

3-25

9

DE

TRO

IT &

S

EA

TTLE

, US

Cro

ss s

ectio

nal

Obj

ectiv

e:To

det

erm

ine

whe

n an

d w

hy w

omen

cho

se

to d

isbe

lieve

the

pers

onal

ized

bre

ast

canc

er ri

sk n

umbe

rs.

Met

hod:

W

omen

who

se m

edic

al re

cord

s in

dica

ted

that

they

mig

ht b

e at

abo

ve a

vera

ge ri

sk

of d

evel

opin

g br

east

can

cer w

ere

recr

uite

d fro

m th

e H

enry

For

d H

ealth

Sys

tem

in

Det

roit

and

Gro

up H

ealth

in S

eattl

e. T

he

subj

ects

par

ticip

ated

in a

n on

line

prog

ram

w

hich

pre

sent

ed ta

ilore

d in

form

atio

n ab

out i

ndiv

idua

l bre

ast c

ance

r ris

k af

ter

com

plet

ing

BC

RA

T qu

estio

ns a

nd v

iew

ing

deci

sion

aid

on

chem

opre

vent

ion.

The

y w

ere

pres

ente

d w

ith ta

ilore

d ab

solu

te

risk

(BC

RA

T sc

ore)

and

est

imat

e of

risk

re

duct

ion

with

che

mop

reve

ntio

n.Th

e su

bjec

ts w

ere

aske

d w

heth

er th

ey

belie

ve th

e pr

ogra

m w

as p

erso

naliz

ed

and

whe

ther

they

bel

ieve

the

num

bers

. Th

e B

CR

AT

scor

e w

as c

alcu

late

d us

ing

thes

e fa

ctor

s: a

ge, e

thni

city

, per

sona

l ris

k of

bre

ast c

ance

r, ag

e at

firs

t men

ses,

age

at

firs

t liv

e bi

rth, n

umbe

r of fi

rst d

egre

e re

lativ

es w

ho h

ave

had

brea

st c

ance

r, hi

stor

y of

bre

ast b

iops

ies.

Inte

rven

tion:

Per

sona

lized

risk

est

imat

e in

on

e of

two

diffe

rent

way

s:-

1. “

deta

iled

expl

anat

ion”

of

the

fact

ors

that

w

ere

used

to

calc

ulat

e th

eir

tailo

red

risk

num

bers

2.

“sta

ndar

d ta

ilorin

g”,

parti

cipa

nts

wer

e gi

ven

no i

nfor

mat

ion

abou

t ho

w t

heir

risk

num

ber w

as g

ener

ated

n=69

0 w

omen

- at o

r abo

ve a

vera

ge

risk

of d

evel

opin

g br

east

ca

ncer

as

estim

ated

by

the

Bre

ast C

ance

r R

isk

Ass

essm

ent T

ool

(BC

RA

T)(s

core

rang

e 1.

7-19

.1)

- age

rang

e 46

to 7

4 ye

ars

- 97.

6% w

ere

whi

te

•In

fluen

ce o

f int

erve

ntio

n on

bel

iefs

that

the

prog

ram

was

pe

rson

aliz

ed:

- W

omen

who

rece

ived

“det

aile

d in

form

atio

n” w

ere

mor

e lik

ely

to fe

el th

at th

e pr

ogra

m w

as p

erso

naliz

ed c

ompa

red

to th

ose

rece

ivin

g “s

tand

ard

info

rmat

ion”

; with

mea

n(S

D) o

f 4.

85(1

.39)

vs

4.64

(1.4

1), p

=0.0

5 re

spec

tivel

y

•D

isag

reem

ent w

ith ta

ilore

d ris

k :

131

parti

cipa

nts

disa

gree

d w

ith b

oth

risk

num

bers

(1

8.9%

)

Rea

sons

for d

isag

reem

ent (

qual

itativ

e as

sess

men

t)-

Thei

r fam

ily h

isto

ry m

ade

them

eith

er m

ore

or le

ss li

kely

to

dev

elop

bre

ast c

ance

r tha

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ailo

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I liv

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ive

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thy

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ount

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, m

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ifest

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mon

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rapy

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’ con

clus

ion:

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ben

efits

of

tailo

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risk

stat

istic

s m

ay

be a

ttenu

ated

by

a te

nden

cy f

or p

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be

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tha

t th

ese

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ates

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ly t

o th

em p

erso

nally

. D

ecis

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aids

m

ay p

rovi

de r

isk

info

rmat

ion

that

is

not

acce

pted

by

patie

nts,

bu

t ad

dres

sing

the

pat

ient

s’ p

erso

nal c

ircum

stan

ces

may

lead

to

gre

ater

acc

epta

nce


56

LIST OF EXCLUDED STUDIES

1. Brain K, Parsons E, Bennett P et al. The evolution of worry after breast cancer risk assessment: 6 year follow up of the TRACE study cohort. Psycho-oncology 2011.20;984-991.

2. Brain K, Norman P, Gray J et al. A randomized trial of specialist genetic assessment: psychological impact on women at different levels of familial breast cancer risk. British Journal of Cancer 2002.86;233-238.

3. Antoniou A, Hurdy R, Walker L, et al. Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchaster scoring system using data from UK genetics clinic. J Med Genet 2008. Doi:10.1136/jmg.2007.056556

4. Fischer C, Kuchenbäcker K, Engel C, et al. Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium. J Med Genet 2013; 50:360–367. doi:10.1136

5. McCarthy AM, Armstrong K, Handor E, et al. Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women. Breast Cancer Res Treat. 2013 April; 138(3): 889–898. doi:10.1007/s10549-013-2471-8.

6. Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast cancer risk models. N Engl J Med 2010.362;986-93

7. Mealiffe ME, Stokowski RP, Rhees BK, et al. Assessment of Clinical Validity of a Breast Cancer Risk Model Combining Genetic and Clinical Information. J Natl Cancer Inst 2010.102;1618–1627. DOI: 10.1093/jnci/djq388

8. Fasching PA, Bania MR, Carolin Nestle-Kra¨mling CN, et al. Evaluation of mathematical models for breast cancer risk assessment in routine clinical use. European Journal of Cancer Prevention 2007. 16;216-224

9. Sta°hlbom AK, Johansson H, Liljegren A, et al. Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer. Familial Cancer 2012.11:33–40. DOI 10.1007/s10689-011-9495-1

10. Darabi H, Czane K, Zhao W, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast cancer research 2012.14;R25

11. Maclnnis RJ, Bickerstaffe A, Apicella C et al. Prospective validation of the breast cancer risk prediction model BOADICEA and a batch-mode version BOADICEACentre. British Journal of Cancer.2013;109:1296-1301

12. Zheng W, Wen W, Gao YT et al. Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women. J Natl Cancer Inst. 2010;102:972-981

13. Dai J, Hu Z, Jiang Y, et al. Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Research 2012, 14:R17

14. Weisstock CR, Rajapakshe R, Bitgood C, et al. Assessing the Breast Cancer Risk Distribution for Women Undergoing Screening in British Columbia. Cancer Prev Res 2013. 6(10); 1084–92 DOI: 10.1158/1940-6207.CAPR-13-0027

15. Sharabi SE, Bullocks JM, Dempsey PJ, et al. The need for breast cancer screening in women undergoing elective breast surgery: an assessment of risk and risk factors for breast cancer in young women. Aesthetic Surgery Journal 2010. 30(6);821-831.DOI:10.1177/1090820X10386589

16. Smith RP, Ni X, Muram D. Breast caner risk assessment: positive predictive value of family history as a predictor of risk. Menopause 2011. 18(6);621-624.

17. Park S, Cochrane B, Koh SB, et al. Comparison of breast cancer risk estimations, risk perception and screening behaviour in obese rural Korean women. Oncology Nursing Forum 2011. 30(6);E394-401.

18. Boughey JC, Hartmann LC, Anderson SS, et al. Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) Model for Breast Cancer Risk Prediction in Women With Atypical Hyperplasia. J Clin Oncol 2010. 28:3591-3596.

19. Ozanne EM, Drohan B, Bosinoff P, et al. Which Risk Model to Use? Clinical Implications of the ACS MRI Screening Guidelines. Cancer Epidemiol Biomarkers Prev; 22(1); 146–9.

20. Seyednoori T, Pakseresht S. Risk of developing breast cancer by utilizing Gail model. Women & Health. 2012;52:391-402

21. Gail MH, Constantino JP, Pee D, et al. Projecting Individualized Absolute Invasive breast cancer risk in African American women. J Natl Cancer Inst 2007.99;1782-92.

22. Rockhill B, Spiegelman D, Byrne C, et al. Validation of the Gail et al model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst 2001.93(5);358-66.


57

23. Tamimi RM, Rosner B, Colditz GA, et al. Evaluation of a breast cancer risk prediction model expanded to include category of prior benign breast disease lesion. Cancer 2010.116(21);4944-53.doi:10.1002/cncr.25386.

24. Decarli A, Calza S, Masala G, et al. Gail model for prediction of absolute risk of invasive breast cancer: independent evaluation in the Florence-European Prospective investigation into cancer and nutrition cohort. J Natl Cancer Inst 2006.98(23);1686-1693.

25. Newman LA. Applicability of the Gail model for breast cancer risk assessment in Turkish female population and evaluation of breast feeding as a risk factor. Breast cancer res treat 2010. 120;425-26.

26. Tice JA, Cummings SR, Smith-Bindman R, et al. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new prediction model. Ann Intern Med 2008.148(5);337-347.

27. Dorval M, Bouchard K, Chiquette, et al. A focus group study on breast cancer risk presentation: one format does not fit all. European Journal of human genetics 2013.21;719-724.

28. Quillin JM, Fries E, McClish D, et al. Gail model risk assessment and risk perceptions. Journal of behavioural medicine 2004.27(2);205-214.

29. Crepeau AZ, Willoughby L, Pinsky B, et al. Accuracy of personal breast cancer risk estimation in cancer free women during primary care visits. Women & Health 2008.47(2);113-130.DOI:10.1080/03630240802092431

30. Ceber E, Soyer MT, Ciceklioglu M, et al. Breast cancer risk assessment and risk perception on nurses and midwives in Bornova Health District in Turkey. Cancer Nursing 2006.29(3);244-249.

31. Neumann PJ, Cohen JT, Hammitt JK, et al. Willingness to pay for predictive test with no immediate treatment implications: a survey of US residents. Health Econ 2012.21;238-251.

32. Ozanne EM and Esserman LJ. Evaluation of breast cancer risk assessment techniques: a cost effectiveness analysis. Cancer epidemiol Biomarkers Prev 2004.13(12);2043-2051.

33. Panchal SM, Ennis M, Canon S, et al. Selecting a BRCA risk assessment model for use in a familial cancer clinic. BMC Medical Genetics 2008.9(116). doi:10.1186/1471-2350-9-116

34. Kwong A, Wong CHN, Suen DTK, et al. Accuracy of BRCA1/2 mutation prediction models for different ethnicities and genders: experience in a Southern Chinese cohort. World J Surg 2012.36;702-713.

35. Lin Y, Shao N, Zhang YJ, et al. Risk assessment of breast cancer in Guangdong, China: a community based survey. Asian Pacific Journal of Cancer Prevention 2012.13;2759-2763.

36. Brinton JT, Barke LD, Freivogel ME, et al. Breast cancer risk assessment in 64,659 women at a single high volume mammography clinic. Acad Radiol 2012.19(1);95-99.doi:10.1016/j.acra.2011.09.003

37. Mavaddat N, Peock S, Frost D, et al. Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst 2013.105;812-822.

38. Rodrigues JJPC, Reis N, Moutinho JAF, et al. Breast alert: an online tool for predicting the lifetime risk of women breast cancer. J med Syst 2012.36;1417-1424.

39. Rupert DJ, Squiers LB, Renaud JM, et al. Communicating risk of hereditary breast and ovarian cancer with an interactive decision support tool. Patient education and counselling 2013.92;188-196

40. Brennan P, Claber O, Brennan T. Cancer family history triage: a key step in the decision to offer screening and genetic testing. Familial Cancer 2013.12;497-502.

41. Baer HJ, Schneider LI, Colditz GA, et al. Use of a web based risk appraisal tool for assessing family history and lifestyle factors in primary care.J Gen Intern Med 2013.28(6);817-24.

42. Mathieu E, Barratt AL, McGeehan K, et al. Helping women make choices about mammography screening: an online randomized trial of a decision aid for 40 year old women. Patient education and counselling 2010.81;63-72.

43. Rubinstein WS, Acheson LS, O’Neill SM, et al. Clinical utility of family history for cancer screening and referral in primary care: a report from the Family Healthware Impact Trial. Genet Med 2011. 13(11);956-65.

44. Hampel H, Sweet K, Westman JA, et al. Referral for cancer genetics consultation: a review and compilation of risk assessment criteria. J Med Genet 2004.41;81-91.doi:10.1136/jmg.2003.010908

45. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 2001.358;9291;1389-99.

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