Knowledge Engineering in Oncology

Andre DekkerDepartment of Radiation Oncology (MAASTRO)GROW - Maastricht University Medical Centre +Maastricht, The Netherlands

SLIDES AVAILABLE ON SLIDESHARE (slideshare.net/AndreDekker)

2

Disclosures• Research collaborations incl. funding and speaker honoraria

– Varian (VATE, SAGE, ROO, chinaCAT, euroCAT), Siemens (euroCAT), Sohard (SeDI, CloudAtlas), Mirada Medical (CloudAtlas), Philips (EURECA, TraIT, SWIFT-RT, BIONIC), Xerox (EURECA), De Praktijkindex (DLRA), ptTheragnostic (DART, Strategy), CZ (My Best Treatment)

• Public research funding– Radiomics (USA-NIH/U01CA143062), euroCAT(EU-Interreg), duCAT&Strategy

(NL-STW), EURECA (EU-FP7), SeDI & CloudAtlas & DART (EU-EUROSTARS), TraIT (NL-CTMM), DLRA (NL-NVRO), BIONIC (NWO)

• Spin-offs and commercial ventures– MAASTRO Innovations B.V. (CSO)– Various patents on medical machine learning

3

Seminar structureBig Data in Radiation Oncology

• Part 1: Rationale • Part 2: Data• Part 3: Modelling• Part 4: Change Practice

Knowledge Engineering in OncologyPart 1: Rationale

5

Can we predict a tulip’s color by looking at the bulb?

http://www.amystewart.com

6

Predicting the color of a tulip - AUC

1.00AUC

0.72

0.50

7

Predicting the survival of NSCLC patients

AUC1.00

AUC0.50

AUC0.72

8

Prediction by MDs?

NSCLC2 year survival30 patients8 MDsRetrospectiveAUC: 0.57

NSCLC2 year survival158 patients5 MDsProspectiveAUC: 0.56

Oberije et al. Kruger et al. 1999

Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence leads to inflated self-assessments. J Pers Soc Psych

9

The problem of Big Data – The doctor is drowning

• Explosion of data• Explosion of decisions• Explosion of

‘evidence’*• 3 % in trials, bias• Sharp knife

*2010: 1574 & 1354 articles on lung cancer & radiotherapy = 7.5 per dayHalf-life of knowledge estimated at 7 years (in young students) J Clin Oncol 2010;28:4268

JMI 2012 Friedman, RigbyBMJ Clinical Evidence

We cannot predict outcomes of individual treatments

10

The potential of Big Data - Rapid Learning Health Care

In [..] rapid-learning [..] data routinely generated through patient care and clinical research feed into an ever-growing [..] set of coordinated databases. J Clin Oncol 2010;28:4268

[..] rapid learning [..] where we can learn from each patient to guide practice, is [..] crucial to guide rational health policy and to contain costs [..].Lancet Oncol 2011;12:933

Examples: DLRA, NROR, CAT (www.eurocat.info) ASCO’s CancerLinQ

Knowledge Engineering in OncologyPart 2: Data

12

Cancer Data?

Oncology2005-2015140M patients0.1-10GB per patient14-1400PB80% unstructured100k hospitals

13

Barriers to sharing data[..] the problem is not really technical […]. Rather, the problems are ethical, political, and administrative. Lancet Oncol 2011;12:933

1. Administrative (I don’t have the resources)2. Political (I don’t want to)3. Ethical (I am not allowed to)

4. Technical (I can’t)

14

A different approach• If sharing is the problem: Don’t share the data

• If you can’t bring the data to the research• You have to bring the research to the data

• Challenges– The research application has to be distributed (trains & track)– The data has to be understandable by an application (i.e. not a human) ->

FAIR data stations

15

CORAL: Community in Oncology for RApid Learning

7

4

meerCATLung - DyspneaU MichiganMAASTROThe Christie

Map © Copyright Showeet.com

canCATLung SBRT - ControlPrincess MargaretMAASTRO

BIONICRadiomicsMAASTROTata Memorial

duCATLung - DysphagiaMAASTRORadboudNKI

euroCATLung - SurvivalUK AachenLOC HasseltCatharinaMAASTROCHU Liege

Interest to joinErasmus (Breast)BCCA (Breast)Bloemfontein (Cervix)Odense (HN, Lung)Aalst (Lung)McGill (Brain)

ozCATHead&Neck - Survival LiverpoolIllawarra NewcastleWestmeadMAASTRORTOG/NRG

worldCATRectum - Local ControlFudanRome/EURTOG/NRG

Knowledge Engineering in OncologyPart 3: Modelling

17

Modelling

Lambin et al doi:10.1038/nrclinonc.2012.196

18

TRIPOD

https://www.tripod-statement.org/TRIPOD

Knowledge Engineering in OncologyPart 4: Changing practice

20

Lung cancer -> DESERT trial

PalliativeRT/Chemo

Radical RT

SequentialChemo-RT

ConcurrentChemo-RT

EscalatedChemo-RT

100%

50%50%

21

Model based approach• Proton therapy introduction in the Netherlands• Expensive and only 1800 slots• ALARA -> protons for reduced toxicity• RCT -> protons for better survival/control• Evidence-based (e.g. paediatric) and model-based

indications (HN, GBM, Lung, Breast, Prostate)

Widder et al. http://dx.doi.org/10.1016/j.ijrobp.2015.10.004

22

Model based approach

Widder et al. http://dx.doi.org/10.1016/j.ijrobp.2015.10.004

Cheng et al. http://dx.doi.org/10.1016/j.radonc.2015.12.029

23

There is an app for that

24

Next iteration -> Personal Health Train: Get citizens in control• https://www.youtube.com/watch?v=mktAtHmy-FM

25

Acknowledgements• Fudan Cancer Center, Shanghai,

China• Varian, Palo Alto, CA, USA• Siemens, Malvern, PA, USA• RTOG, Philadelphia, PA, USA• MAASTRO, Maastricht, Netherlands• Policlinico Gemelli, Roma, Italy• UH Ghent, Belgium• UZ Leuven, Belgium• Radboud, Nijmegen, Netherlands• University of Sydney, Australia• University of Michigan, Ann Arbor,

USA

• Liverpool and Macarthur CC, Australia

• CHU Liege, Belgium• Uniklinikum Aachen, Germany• LOC Genk/Hasselt, Belgium• Princess Margaret CC, Canada• The Christie, Manchester, UK• UH Leuven, Belgium• State Hospital, Rovigo, Italy• Illawarra Shoalhaven CC, Australia • Catharina Zkh Eindhoven,

Netherlands• Philips, Eindhoven, NetherlandsMore info on: www.predictcancer.org www.cancerdata.org

www.eurocat.info www.mistir.info

Thank you for your attention

Andre DekkerDepartment of Radiation Oncology (MAASTRO)GROW - Maastricht University Medical Centre +Maastricht, The Netherlands