canaadian jtouhrnal ofolog y volume 5, issue 2 summer 2013 · 2020. 7. 6. · digital pathology...

Digital Pathology

Canadian Journal of

Pathology

Publications Agreement Number 40025049 • ISSN 1918-915X

Official Publication of the Canadian Association of Pathologists

www.cap-acp.org

Volume 5, Issue 2 • Summer 2013

tal Pathology

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VOLUME 5, ISSUE 2 2013

About the Cover

44 Guest Editorial: The Digital Laboratory Ruth F. Padmore, MD, FRCPC, PhD

46 Éditorial – Collaboration spéciale : Le laboratoire numérique Ruth F. Padmore, MD, FRCPC, PhD

Original Articles48 The Use of Digital Imaging for Hematology Proficiency Testing Ruth F. Padmore, MD, FRCPC, PhD, Anne Raby, MLT, ART

51 Image Analysis Solutions for Automatic Scoring and Grading of Digital Pathology Images April Khademi, PhD, PEng

56 Implementation of CellaVision at a Consolidated Laboratory Medicine Program Wendy Patterson, ART, Teresa DiFrancesco, MLT

60 “Progressing” Multicystic Mesothelioma of the Liver Mara Caragea, MD, Pamela Smith, MD, BSc, MBChB, FRCP(C), Christopher J. Howlett, MD, PhD, FRCP(C), Jeremy R. Parfitt, MD, FRCPC, Subrata Chakrabarti, MD, PhD, FRCP(C)

65 Laboratory Utilization Trends: Past and Future Megan-Joy Rockey, BHSc, Christopher Naugler, MD, Davinder Sidhu, LLB, MD

Current Review72 Matrix-Assisted Laser Desorption Ionization – Time-of-Flight Mass Spectroscopy in the Clinical Microbiology Laboratory Ross Davidson, PhD, FCCM, D(ABMM)

Correspondence78 Pathological Reporting of Colorectal Polyps: Pan-Canadian Consensus Guidelines – A Second Opinion Michael Bonert, MASc, MD, FRCPC, Samir C. Grover, MD, MEd, FRCPC

79 Response David K. Driman, MBChB, FRCPC, Victoria A. Marcus, MD, FRCPC, Robert J. Hilsden, MD, PhD, FRCPC, David A. Owen, MB, FRCPC

Professional Development/Employment Opportunities59 Vancouver Coastal Health

EDITOR-IN-CHIEFJ. Godfrey Heathcote, MA, MB BChir, PhD, FRCPC

EDITORIAL BOARDManon Auger, MD, FRCPC, Cytopathology;

Calvino Cheng, BSc, MD, FRCPC, Pathology Informatics and Quality Management;

David K. Driman, MB ChB, FRCPC, Anatomical Pathology;

Todd F. Hatchette, BSc, MD, FRCPC, Medical Microbiology; Michael J. Shkrum, MD, FRCPC, Forensic Pathology;

MANAGING EDITORSusan Harrison

COPY EDITORS Scott Bryant, Susan Harrison

PROOFREADERScott Bryant

ART DIRECTORAndrea Brierley, [email protected]

TR ANSL ATORSJulie Paradis, Marie Dumont

SALE S AND CIRCUL ATION COORDINATORBrenda Robinson, [email protected]

ACCOUNTINGSusan McClung

GROUP PUBLISHERJohn D. Birkby, [email protected]

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Canadian Journal of Pathology • Volume 5, Issue 2, 2013

Contents

For Instructions to Authors, please visit www.andrewjohnpublishing.com/authors_cjp.html

The cover image shows acute myelomonocytic leukemia with abnormal eosinophils.

FOUNDING EDITORJagdish Butany, MBBS, MS, FRCPC

Pierre Douville, MD, FRCPC, Medical Biochemistry;

Lawrence Haley, MD, FRCPC, Hematopathology;

Canadian Journal of P athology 43Summer 2013

Summer 201344 Canadian Journal of P athology

Competing interests: None declared

GUEST EDITORIAL

Digital imaging/virtual microscopy is increasingly usedin the pathology laboratory. In October 2012, asymposium titled The Digital Laboratory: Today and

Tomorrow took place in Hamilton, Ontario, organized by

the Hamilton Regional Laboratory Medicine Program,

Quality Management Program – Laboratory Services

(QMP-LS), and the Institute for Quality Management in

Healthcare. This was an exciting day, with nine presentations

covering a variety of practical and theoretical aspects of

digital pathology in the diagnostic laboratory. In this issue

of the Canadian Journal of Pathology, some of the speakers

have contributed summaries of their presentations at this

meeting.

Two of the presentations focused on current applications of

digital imaging to telepathology in the laboratory.

Dr. Andrew Evans, of University Health Network (UHN), in

Toronto, Ontario, presented on current clinical applications

and barriers to adoption of digital pathology. Since 2006,

UHN has used whole-slide imaging in an uncomplicated

workflow situation for neuropathology intra-operative

consultations in the absence of an on-site pathologist. The

performance of this system has matched or exceeded that of

traditional light microscopy.1 UHN has since expanded its

use of this technology to provide consultation and primary

diagnostic services. Historical barriers to the adoption of

digital microscopy were also discussed, including cost, speed,

data management, regulatory and medical-legal issues, a lack

of best practice guidelines, and pathologist perception of

inferior performance relative to light microscopy. It must be

noted that most or all of these barriers are surmountable. At

both national and international levels, guidelines for best

practices and the implementation of digital pathology for

clinical use will be released by mid-2013, most notably from

the Canadian Association of Pathologists, the College of

American Pathologists, and the Digital Pathology

Association. There is also a growing body of peer-reviewed

literature from a variety of institutions demonstrating the

diagnostic equivalence and/or non-inferiority of digital

pathology systems when compared with light microscopy.

These validation studies are essential in order to reassure

pathologists, clinicians, and patients that digital pathology

systems can be used to provide accurate diagnoses. Finally,

digital pathology vendors are actively working with

regulatory bodies such as Health Canada and the Food and

Drug Administration (FDA) in the United States to secure

approval to market digital pathology platforms for

diagnostic purposes.

Mr. Tedd Kelemen’s presentation described the realization

of a regional telepathology project in Eastern Quebec.2 It

outlined the achievement of the specific objectives of

provision of intra-operative consultations and expert second

opinions, as well as a fast turnaround time for

immunohistochemical studies.

Three presentations highlighted the further application of

digital imaging in diagnostic pathology. Dr. Tim Feltis, of

Credit Valley Hospital in Mississauga, Ontario, described the

use of scanned slides to support multidisciplinary cancer

conferences. Ms. Teresa Di Francesco, MLT, and Ms. Wendy

Patterson, MLT, ART, of the Hamilton Regional Laboratory

Medicine Program, described the successful implementation

of peripheral blood white blood cell (WBC) differential

counts using automated digital image analysis.3 Benefits of

this include regional access to results, standardization of

reporting, and mitigation of the effect of shortages of

experienced medical laboratory technologists. Dr. Terence

Colgan, of Mount Sinai Hospital, Toronto, shared lessons

learned from computer-assisted automation of

gynecological cytology.4 In Ontario, 10–15% of

Papanicolaou tests are reviewed using only automated

digital cytology, without any interpretation by technologists.

To achieve this remarkable result, rigorous quality control

mechanisms have been developed and are adhered to.

There were two presentations on the use of digital imaging

for proficiency testing in Ontario through the QMP-LS

The Digital Laboratory


PADMORE

programs: Dr. Golnar Rasty, of UHN, on cytology

proficiency testing and Dr. Ruth Padmore, of The Ottawa

Hospital, on hematology proficiency testing. The use of

digital images for external quality assessment (EQA)

programs in cytology is especially valuable for assessment

and/or education when only a small volume of unique

material is available. Features that users have identified as

important for successful digital EQA in cytology include

image quality and the abilities to focus and to mark/track

the slide.5 The QMP-LS experience for hematology EQA was

presented. Using similar or identical hematology cases as

glass slide surveys and as scanned whole-slide images, the

QMP-LS surveys have shown comparable performance by

participating laboratories. These results are similar to the

concordance in performance between glass slide and whole-

slide images in the UK National External Quality Assurance

Scheme for hematology.6

Two presentations focused on image analysis algorithms. Dr.

Aaron Pollett, of Mount Sinai Hospital, Toronto, reviewed

the advantages of image analysis (automation, consistency)

where this is useful, including immunohistochemical/

immunofluorescent quantification and the evaluation of

rare events, for example, the identification of organisms on

slides. The digital laboratory of the future may incorporate

algorithms to aid in diagnosis, such as those proposed by the

recent winner of the Google Science Fair 2012, where the

analysis of nine cellular attributes of breast fine-needle

aspirate specimens was a high predictor for the presence of

malignancy.7 Dr. April Khademi, of GE Healthcare’s

Innovation Centre of Excellence (PICOE), in Calgary,

Alberta, reviewed quantitative digital pathology challenges

and revealed the computer algorithms that underlie this

application, including grey-scale and colour digital image

algorithms, segmentation (for cell counting and membrane

detection), and extraction (for quantification of nuclei).8

Ruth F. Padmore, MD, FRCPC, PhDStaff Hematopathologist, The Ottawa HospitalAssociate Professor, University of OttawaChair, QMP-LS Hematology Scientific Committee

On behalf of the meeting planning group:Mr. Ron Giesler (chair), ART, QMP-LSDr. Judit Zubovits, Scarborough Hospital, TorontoDr. Golnar Rasty, UHN, Toronto General HospitalAndrea Tjahja, ART, BSc, BEd, Hamilton Regional Labora-tory Medicine ProgramDr. Ruth F. Padmore, The Ottawa Hospital

References1. Evans AJ, Kiehl TR, Croul S. Frequently asked questions concerning the use

of whole-slide imaging telepathology for neuropathology frozen sections. Semin Diagn Pathol 2010;27(3):160–6.

2. Têtu B, Fortin JP, Gagnon MP, Louahlia S. The challenges of implementing a“patient-oriented” telepathology network; the Eastern Québec telepathologyproject experience. Anal Cell Pathol (Amst) 2012;35(1):11–8.

3. Briggs C, Longair I, Slavik M, et al. Can automated blood film analysis replacethe manual differential? Int J Lab Hematol 2009;31(1):48–60.

4. Colgan TJ, Bon N, Clipsham S, et al. A validation study of the FocalPoint GSimaging system for gynecologic cytology screening. Cancer Cytopathol 2013;121(4):189–96. doi: 10.1002/cncy.21271. Epub 2013 Jan 29; http://onlinelibrary.wiley.com/doi/10.1002/cncy.21271/pdf. Accessed March19, 2013.

5. Stewart J, Miyazaki K, Bevans-Wilkins K, et al. Virtual microscopy for cytologyproficiency testing. Cancer Cytopathol 2007;111:203–9.

6. Burthem J, Brereton M, Ardern J, et al. The use of digital ‘virtual slides’ in thequality assessment of haematological morphology: results of a pilot exercise involving UK NEQAS(H) participants. Br J Haematol 2005;130:293–6.

7. Wenger B. Cloud for cancer breast cancer detection; HYPERLINK "http://cloud4cancer.appspot.com/" http://cloud4cancer.appspot.com/. Accessed February 25, 2013.

8. Lu C, Mahmood M, Jha N, Mandal M. A robust automatic nuclei segmentation technique for quantitative histopathologic image analysis. AnalQuant Cytol Histol 2012;34(6):296–308.

L’imagerie numérique ou la microscopie virtuelle est deplus en plus utilisée dans les laboratoires de pathologie.En octobre 2012 eut lieu à Hamilton, Ontario, un

symposium intitulé Le laboratoire numérique : aujourd’hui

et demain, organisé par le Hamilton Regional Laboratory

Medicine Program (programme régional de médecine de

laboratoire de Hamilton), le Programme de gestion de la

qualité - Services de laboratoire (PGQ-SL) et l’Institute for

Quality Management in Healthcare (institut pour la gestion

de la qualité en soins de santé). Ce fut une journée

passionnante, comportant neuf présentations couvrant

différents aspects pratiques et théoriques de la pathologie

numérique dans le laboratoire de diagnostic. Dans le présent

numéro de la Revue canadienne de pathologie (RCP),

quelques-uns des conférenciers présents à cette rencontre

nous font bénéficier d’un résumé de la présentation qu’ils

ont alors offerte.

Deux des présentations ont porté principalement sur les

applications actuelles de l’imagerie numérique à la

télépathologie en laboratoire. Le Dr Andrew Evans, du

Réseau universitaire de santé (University Health Network

ou UHN), Toronto, Ontario, nous a fait un exposé sur les

applications cliniques actuelles de la pathologie numérique

et les obstacles à l’adoption de cette dernière. Il nous a

informé que depuis 2006, le Réseau utilise l’imagerie plein

champ pour des activités simples liées à des consultations

peropératoires en neuropathologie en l’absence d’un

pathologiste sur les lieux. Le rendement de ce système

équivaut à celui de la microscopie optique classique ou le

surpasse1. Le Réseau a depuis élargi l’utilisation qu’il fait de

cette technologie pour pouvoir offrir des services de

consultation et de diagnostic primaire. Les obstacles

historiques à l’adoption de la microscopie numérique y

furent aussi discutés, notamment les coûts, la vitesse, la

gestion des données, les enjeux médicolégaux et de

réglementation, le manque de lignes directrices sur les

pratiques exemplaires ainsi que la perception qu’ont les

pathologistes d’un rendement moindre par rapport à la

microscopie optique. Il y a lieu de noter que la plupart ou

même l’ensemble de ces obstacles sont surmontables. Au

niveau national et international, des lignes directrices sur les

pratiques exemplaires et sur la mise en place de la pathologie

numérique pour utilisation clinique seront publiées d’ici le

milieu de l’année 2013, plus particulièrement par

l’Association canadienne des pathologistes, le College of

American Pathologists et la Digital Pathology Association

(DPA). Il y a aussi une quantité croissante de documentation

évaluée par les pairs de différentes institutions qui fait état

de l’équivalence sinon de la non-infériorité de la qualité des

systèmes de pathologie numérique en matière de diagnostic

par rapport à la microscopie optique. Ces études de

validation sont essentielles pour rassurer les pathologistes,

les cliniciens et les patients sur le fait que les systèmes de

pathologie numérique peuvent être utilisés pour obtenir des

diagnostics précis. Enfin, les fournisseurs de pathologie

numérique travaillent activement avec les organismes de

réglementation comme Santé Canada et la Food and Drug

Administration (FDA) des États-Unis pour obtenir

l’autorisation de commercialiser les plateformes de

pathologie numérique à des fins de diagnostic.

Dans son exposé, M. Tedd Kelemen nous a entretenu de la

réalisation d’un projet régional de télépathologie dans l’Est

du Québec2. Il nous a livré un bref compte rendu de

l’atteinte d’objectifs spécifiques en matière de consultations

peropératoires, d’obtention d’un deuxième avis médical et

d’un court délai d’exécution pour les études

immunohistochimiques.

Trois présentations ont attiré notre attention sur d’autres

applications de l’imagerie numérique en pathologie

diagnostique. Le Dr Tim Feltis, de l’Hôpital Credit Valley,

Mississauga, Ontario, nous a expliqué comment on utilise

des lames numérisées en appui à des conférences

multidisciplinaires sur le cancer. Mme Teresa Di Francesco,

TLM, et Mme Wendy Patterson, TLM, ART (certification

avancée), du Hamilton Regional Laboratory Medicine

Program, nous ont décrit la mise en application réussie de

la numération de formules leucocytaires de sang

périphérique au moyen de de l’analyse automatisée d’images


Aucun conflit d’intérêts à déclarer

ÉDITORIAL – COLLABORATION SPÉCIALE

Le laboratoire numérique


PADMORE

numériques3. Cette méthode présente plusieurs avantages

dont : l’accès régional aux résultats; la normalisation des

rapports; l’atténuation des effets du manque de

technologistes de laboratoire médical expérimentés. Le

Dr Terence Colgan, de l’Hôpital Mount Sinai, Toronto, nous

a fait part des enseignements tirés de l’automatisation

assistée par ordinateur des cytologies gynécologiques4. En

Ontario, de 10 à 15 % des tests Pap sont vérifiés uniquement

par une cytologie numérique automatisée, sans

l’interprétation d’un technologiste. Pour atteindre ce résultat

remarquable, des mécanismes de contrôle de qualité

rigoureux ont été élaborés et sont respectés.

Il y a également eu deux présentations sur le recours à

l’imagerie numérique pour les épreuves de compétence en

Ontario par le PGQ-SL : avec la Dre Golnar Rasty, du Réseau

universitaire de santé, pour les épreuves de compétences en

cytologie, et avec la Dre Ruth Padmore, de l’Hôpital

d’Ottawa, pour les épreuves de compétence en hématologie.

L’utilisation d’images numériques pour les programmes

d’évaluation externe de la qualité (EEQ) en cytologie est

particulièrement utile pour l’évaluation et/ou

l’enseignement lorsque seulement un petit volume de

matériel unique est disponible. Parmi les éléments que les

utilisateurs ont jugé comme étant importants pour la

réussite d’une EEQ numérique en cytologie, mentionnons

la qualité de l’image et la capacité de mettre au point la lame,

de la marquer et d’en faire le suivi5. Pour ce qui est de l’EEQ

en hématologie, on nous a présenté l’expérience du PGQ-

SL. Le PGQ-SL a conduit des études sur des épreuves de

compétence en hématologie pour comparer les résultats

obtenus à partir de lames de verre et ceux obtenus à partir

d’images plein champ numérisées, et ce, pour des cas

identiques ou similaires. Les études ont démontré

l’obtention de performances comparables par les

laboratoires participants. Ces constats sont semblables à

ceux obtenus au Royaume-Uni par le National External

Quality Assurance Scheme for hematology relativement au

niveau de concordance de performance entre examens de

lames de verre et examens d’images numérisées6.

Par ailleurs, deux présentations étaient axées sur les

algorithmes d’analyse d’images. Le Dr Aaron Pollett, de

l’Hôpital Mount Sinai, Toronto, a passé en revue les

avantages de l’analyse d’images (automatisation, uniformité)

et les cas où celle-ci s’avère utile, notamment pour la

quantification immunohistochimique ou immuno-

fluorescence et l’évaluation d’éventualités rares, comme

l’identification d’organismes sur lame. Le laboratoire

numérique de l’avenir peut intégrer des algorithmes pour

aider à établir un diagnostic, comme ceux proposés par le

récent gagnant de la Google Science Fair 2012 et grâce

auxquels l’analyse de neuf caractéristiques cellulaires de

prélèvements de sein obtenus par aspiration à l’aiguille fine

s’est avérée être un important indicateur prévisionnel de la

présence de malignité7. Mme April Khademi, ing., D.Sc., du

GE Healthcare’s Pathology Innovation Centre of Excellence

(PICOE), à Calgary, Alberta, a passé en revue les défis

quantitatifs de la pathologie numérique et a présenté les

algorithmes informatiques sous-jacents à cette application,

notamment les algorithmes des images numériques de

couleur et de la gamme de gris, de segmentation (pour le

dénombrement cellulaire et la détection de membranes

cellulaires) et de l’extraction (pour la quantification du

noyau)8.

Ruth F. Padmore, M.D., FRCPC, Ph.D.Hématopathologiste, membre du personnel, Hôpital d’OttawaProfesseure agrégée, Université d’OttawaPrésidente du Comité scientifique du PGQ-SL en hématologie

Au nom du groupe de planification de la rencontre :M. Ron Giesler (président), ART, PGQ-SLDre Judit Zubovits, Hôpital de Scarborough, TorontoDre Golnar Rasty, UHN, hôpital général de TorontoAndrea Tjahja, ART, B.Sc., B.Ed., Hamilton Regional Laboratory Medicine ProgramDre F. Ruth Padmore, Hôpital d’Ottawa

Référencesvoyez p. 45


The Use of Digital Imaging for Hematology Proficiency Testing

Ruth F. Padmore, MD, FRCPC, PhD, is a member of the Department of Pathology and Laboratory Medicine, The OttawaHospital and University of Ottawa, in Ottawa, Ontario. Anne Raby, MLT, ART, is the hematology consultant technologist forthe Quality Management Program-Laboratory Services. Correspondence may be directed to [email protected] article has been peer reviewed.Competing interests: The authors have no competing interests, financial or otherwise.The photomicrographs used are the property of Quality Management Program-Laboratory Services (QMP-LS). The use ofthese photomicrographs is not permitted without written permission from QMP-LS.

Ruth F. Padmore, MD, FRCPC, PhD, Anne Raby, MLT, ART

ABSTRACTThis article summarizes the results of four hematology proficiency testing surveys carried out

from 2010 to 2012 by Quality Management Program – Laboratory Services (QMP-LS) using

digitally scanned slides. The surveys included digital images from two peripheral blood films

distributed to approximately 180 laboratories, and digital images from two bone marrow

aspirate slides distributed to approximately 60 laboratories in Ontario. The results showed good

concordance of morphological observations and diagnostic statements with the previous glass

slide surveys using identical (three surveys) or similar (one survey) cases. Barriers to the

implementation of digital imaging for proficiency testing surveys include the challenge of

achieving good-quality images of bone marrow aspirate slides.

RÉSUMÉ Le présent article résume les résultats de quatre études portant sur des épreuves de compétence

en hématologie effectuées de 2010 à 2012 par le Programme de gestion de la qualité – Services

de laboratoire (PGQ-SL) au moyen de lames numérisées par balayage optique. Les études

comprenaient des images numériques de deux films de sang périphérique distribuées dans

environ 180 laboratoires et des images numériques de deux lames d’un aspirat de mœlle osseuse

distribuées à environ 60 laboratoires dans la province de l’Ontario. Les résultats montrent une

bonne concordance des observations morphologiques et des diagnostics formulés avec les

observations et diagnostics d’études antérieures utilisant des lames de verre pour des cas

identiques (trois études) ou similaires (une étude). Un des obstacles à l’application de l’imagerie

numérique dans les études sur les épreuves de compétence réside dans la difficulté d’obtenir

des images de lames d’aspirat de moelle osseuse qui soient de bonne qualité.

ORIGINAL ARTICLE

Digital scanning of hematology slides (peripheral blood andbone marrow) can attain an image quality similar to thatobtained by direct viewing using a microscope.1 A pilot survey

for using digital images in hematology proficiency testing has

been reported by the UK National External Quality

Assessment Scheme for General Haematology, UK

NEQAS(H). In that study, the digital images were of small

field size, only 20 stitched images, resulting in possible field

selection bias by the operator selecting the fields for scanning.2

This article summarizes the results of four hematology

proficiency testing surveys carried out from 2010 to 2012 by

Quality Management Program – Laboratory Services

(QMP-LS) using digitally scanned slides.

MethodsThe slides were scanned at magnification of 83× (case 3) or


PADMORE AND RABY

100× oil immersion (cases 1, 2, and 4) (Aperio Technologies).

An approximately 2 cm × 2 cm area of each slide was scanned.

Web hosting was performed using Aperio’s Spectrum Basic

Hosting Service (now called eSlide Manager), and the images

were viewed using Aperio’s ImageScope software. Four

morphology cases were chosen, using identical cases (three

surveys) or similar cases (one survey). The morphological

descriptive statements and the diagnostic statements from the

digital image surveys were compared with the previous glass

slide survey results. The surveys included questions to gather

participant feedback on possible barriers to using digital

imaging for proficiency testing. For the peripheral blood slide

surveys, each of the approximately 200–257 participating

laboratories received a Wright-Giemsa stained peripheral

blood film from the same patient on a glass slide. For the bone

marrow surveys, there were 20 bone marrow aspirate slides

available from each case; these were shared among

approximately 60 participating laboratories. Each laboratory

received a single Wright-Giemsa stained bone

marrow aspirate slide on a rotational basis.

Results and DiscussionThe cases used in this study are summarized

in Table 1, and a comparison of key

morphological features and diagnostic

statements is shown in Table 2.

The surveys show similar results for reporting

of key morphological descriptive statements

and correct diagnoses, with the best

concordance achieved with the peripheral

blood cases. The largest discrepancy was with

the cases of acute myelomonocytic leukemia

with abnormal eosinophils, with more

participants arriving at the correct diagnosis

when using the digital image. This might have

occurred because the best bone marrow

aspirate slide was used for scanning (selection bias).

Photographs of the glass slides and snapshots captured from

the online images viewed using ImageScope are shown side

by side for comparison in Figure 1 and show equivalent image

quality.

In reviewing these cases, it was noted that the number of

laboratories in Ontario licensed for complete blood count and

peripheral blood film morphology participating in the

peripheral blood surveys has declined from approximately

257 laboratories in 1999 to approximately 175 laboratories in

2012. This may be a reflection of the consolidation of

laboratories in order to become more efficient and cost-

effective.3 The digital image surveys were considered

voluntary and were not scored. Of licensed Ontario

laboratories, approximately 10% opted out of reporting

results in the two peripheral blood digital surveys and

approximately 20% opted out of reporting results in the two

bone marrow digital surveys. The surveys included questions

Table 1. Cases Used in Digital Image Hematology Proficiency Testing Surveys

QMP-LS Number QMP-LS NumberCase Number Diagnosis (Glass Slide Survey) (Digital Imaging Survey)1. Peripheral blood Malaria (Plasmodium falciparum) M-9902 MORP-1011-MW*2. Peripheral blood Hemolytic uremic syndrome MORP-0611 MORP-1201-DM*3. Bone marrow aspirate Chronic myelogenous leukemia, HEMA-0902-BM HEMA-1007-BW†

chronic phase4. Bone marrow aspirate Acute myelomonocytic leukemia with HEMA-0507-BM BONE-1202-DM*

abnormal marrow eosinophils (M4Eo)*Identical cases to glass slide survey.†Similar cases.

Table 2. Comparison of Key Features: Morphology and Diagnosis

Case 1. Plasmodium falciparum Glass Slide Digital Image% reporting important morphological 98 100descriptive statement: positive parasitemia level% reporting correct malaria speciation (falciparum) 64 76

Case 2. Hemolytic Uremic Syndrome Glass Slide Digital Image% reporting important morphological 99 100descriptive statement: presence of schistocytes% reporting correct diagnosis 96 93

Case 3. Chronic Myelogenous Leukemia Glass Slide Digital Image% reporting important morphological 89 75descriptive statement: blast cells normal in number% reporting correct diagnosis 89 77

Case 4: Acute Myelomonocytic Leukemia Glass Slide Digital Imagewith Abnormal Eosinophils (M4Eo)% reporting correct precise diagnosis (M4Eo) 18 43% reporting diagnosis of acute leukemia 77 82


THE USE OF DIGITAL IMAGING FOR HEMATOLOGY PROFICIENCY TESTING

designed to gather feedback from the participating

laboratories. As the participants gained more experience with

digital imaging proficiency surveys, 91% of laboratories

already had the viewing software downloaded and only 7%

experienced difficulties in downloading the software. Image

quality of the scanned slides for peripheral blood films was

considered good to excellent by up to 83% of participants, but

was suboptimal for the scanned slides for bone marrow

aspirates, with only 40% considering the image quality good

to excellent.

References1. Hutchinson CV, Brereton ML, Burthem J. Digital imaging of haematological

morphology. Clin Lab Haem 2005;27:357–62.

2. Burthem J, Brereton M, Ardern J, et al. The use of digital ‘virtual slides’ in the

quality assessment of haematological morphology: results of a pilot exercise

involving UK NEQAS(H) participants. Br J Haematol 2005;130:293–6.

3. Bossuyt X, Verweire K, Blanckaert N. Laboratory medicine: challenges and

opportunities. Clin Chem 2007;53(10):1730–3. Figure 1. Side-by-side comparison of photomicrographs from glassslides (100× oil immersion; left panels) and snapshots of scannedslides (right panels). Photomicrographs reproduced withpermission from QMP-LS. © 2013 QMP-LS, a department of theOntario Medical Association. All rights reserved.

Case 1. Glass slide: malaria(Plasmodium falciparum)

Case 1. Scanned slide: malaria(Plasmodium falciparum)

Case 2. Glass slide: hemolytic uremic syndrome

Case 2. Scanned slide:hemolytic uremic syndrome

Case 3. Glass slide: chronic myelogenousleukemia

Case 3. Scanned slide: chronicmyelogenous leukemia

Case 4. Glass slide: acutemyelomonocytic leukemia withabnormal eosinophils

Case 4. Scanned slide: acutemyelomonocytic leukemiawith abnormal eosinophils


Image Analysis Solutions for Automatic Scoring and Grading of Digital Pathology

Images

April Khademi, PhD, PEng, is the algorithm development specialist at GE Healthcare’s Pathology Innovation Centre of Excellence (PICOE). Correspondence may be directed to [email protected] article was peer reviewed.Competing interests: The author works for GE Healthcare and owns stocks in the company.

April Khademi, PhD, PEng

ABSTRACTSemi-quantitative grading and scoring systems are used by pathologists to describe metastatic

potential and the likelihood of response to therapy. Unfortunately, the visual review of tissue

slides is a subjective process, creating variability in the grade or score. Moreover, in many cases,

performing manual measurements is laborious, especially if a large number of high-power fields

is required. To combat these challenges, image analysis can be used to automatically analyze

and quantify disease in digital pathology images. Such developments offer objective,

quantitative, reliable, and efficient measures that can improve concordance rates among

pathologists, resulting in better patient management. Also, because of the automated nature of

such systems, image analysis can be exploited in large-scale research studies for drug discovery

or to analyze therapeutic response.

RÉSUMÉ Les pathologistes utilisent des systèmes de classification ou de cotation semi-quantitatifs pour

décrire le potentiel métastatique et la probabilité de réaction à un traitement. Malheureusement,

l’examen visuel des lames des tissus est un processus subjectif, qui crée de la variabilité dans la

classification ou la cotation. En outre, dans bien des cas, la prise de mesures manuelles s’avère

laborieuse, particulièrement si un grand nombre de champs à fort grossissement est requis.

Pour surmonter ces défis, on peut recourir à l’analyse d’images pour analyser et quantifier

automatiquement la maladie au moyen d’images numériques de pathologies. De tels

développements permettent d’obtenir des mesures objectives, quantitatives, fiables et efficaces

qui peuvent améliorer les taux de concordance entre les pathologistes, entraînant ainsi une

meilleure prise en charge du patient. De plus, compte tenu du caractère automatique de ce

système, l’analyse d’images peut être exploitée dans des études de recherche à grande échelle

pour la découverte de médicaments ou pour analyser la réponse thérapeutique.

ORIGINAL ARTICLE

Anatomical pathology is focused on the examination oftissue specimens under magnification. The pathologistdetermines whether a tissue or organ is diseased (diagnosis),

the severity and aggressiveness of the disease (prognosis), and

in many instances, also the likelihood that a specific therapy

will be successful.1 Consequently, pathology provides the

definitive diagnosis and is critical to patient care and

management, and the accuracy, quality, and reliability of such

analyses are of the utmost importance.

There has been some scrutiny of the quality of care and

treatment of patients in some centres in Canada.1 This has

created impetus for the development of quality assurance

(QA) protocols and for governing bodies to ensure that a high

quality of care is delivered through laboratory services. One


AUTOMATIC SCORING AND GRADING OF DIGITAL PATHOLOGY IMAGES

such program operated by the Ontario Medical Association

(OMA) is the Quality Management Program – Laboratory

Services (QMP-LS). This mandatory program is focused on

improving patient safety by assessing the quality of laboratory

test results and ensuring standards of excellence.2 Similar

quality management systems are being pursued in different

regions around the world, including Europe3 and the United

States.4

Quality assurance and improvement plans in surgical

pathology seek to “assure” and “improve” surgical pathology

“products” and can encompass several monitors, including

these:

• Pre-analytical factors (specimen fixation, test ordered,

delivery, accessioning)

• Analytical factors (diagnosis, grading, scoring,

reproducibility, bias)

• Post-analytical factors (transcription, report delivery,

follow-up)

• Turnaround time and clinician satisfaction3,5

These factors touch on a variety of workflow processes, and

new technology is being pursued, such as the integrated digital

pathology (IDP) system developed by Omnyx, a General

Electric–University of Pittsburgh Medical Center (GE-

UPMC) joint venture. The IDP consists of a whole-slide

imaging scanner, archives, and software systems that are fully

integrated into hospital networks. The result is digital

workflow and reporting structures for organized and efficient

case review and management; increased secondary

consultations since colleagues may easily review digital images

remotely; and image interpretation through telepathology for

regions that have limited access to pathology subspecialist

expertise.

Although digital imaging offers increased access to and quality

of care, additional challenges surrounding slide interpretation

still exist. Future technological developments, namely digital

image analysis solutions, hold the potential to solve these

problems by offering objective, reliable, and efficient measures

of disease. This article briefly examines these issues.

Variability of InterpretationOnce a tissue is labelled with a specific diagnosis, the

pathologist proceeds to grade or score the lesion.6 Grading for

a variety of cancerous lesions, such as breast carcinomas, is an

important surrogate marker of metastatic potential that is

used by oncologists when making treatment decisions. In

non-neoplastic conditions, grading or scoring reflects the

activity of the disease and usually relates to inflammatory

and/or fibrotic processes.6 Additionally, grading and scoring

can be used for research purposes, to investigate the effects of

different treatments. Therefore, these semi-quantitative scales

play an important role in patient management, treatment, and

research. Although much research has gone into the

development of grading systems and scoring metrics for

pathology and their utility is clearly understood, some

challenges remain. Aside from psychological issues associated

with the way these systems are developed,6 visual examination

of tissue slides for grading and scoring is a highly subjective

process. There can be mid-to-high discordance rates for slide

interpretation between pathologists, even if they are using the

same semi-quantitative systems.7–13 This creates challenges in

patient management, since patients can receive different

treatment regimens, depending on the slide reviewer.

One of the most commonly discussed discordance rates

surrounds the scoring systems used for Her-2/neu

overexpression. Commonly, pathologists use an H-score to

evaluate overexpression, where the percentage of cells stained

at each intensity score is estimated.14 In a Brazilian study that

analyzed the performance of 149 local laboratories in

determining HER-2/neu overexpression in 500 breast

carcinomas, the study investigators found poor concordance

(171 of 500 cases, 34.2%) between local and reference

laboratories.7 As was noted in that article, many laboratories

lack specialists and, thus, do not have adequate experience in

Her2/neu scoring.7 Similarly, many studies have looked at the

agreement between pathologists for ER/PR intensity and

proportion scores and, although the rates of agreement are

higher than those of Her-2/neu analysis, discordance still

exists.8,9

In other immunohistochemistry (IHC) studies, for example,

on Ki-67 or p53, the scoring methods only depend on the

number of positively stained nuclei, not on the intensity scores.

For example, the Ki-67 proliferation index (PI) is measured by

counting the number of positively stained cells among the total

number of malignant cells. Despite minimal reliance on


KHADEMI

intensity-based scores, the Ki-67 PI has been shown to have

mid-low concordance rates, depending on the tumour grade.

One study showed that among five pathologists, moderate

agreement was found for grade1/grade 3 tumours (κ = 0.56–

0.72) and poor to moderate concordance for grade 2 tumours

(κ = 0.17–0.49).10 Comparably, the concordance between

specialized pathologists and non-specialized pathologists for

p53 scoring was found to be poor in another study (κ= 0.13–

0.25).11 This highlights the subjective nature of the slide

interpretation for IHC analysis, as well as challenges faced by

laboratories that have limited access to subspecialist

pathologists.

Grading systems such as the Nottingham Grading System

(NGS) used for invasive ductal carcinoma in hematoxylin and

eosin–stained breast sections face the same challenges. The

NGS relies on three features to fully describe the tumour:

(1) mitotic count, (2) nuclear grade, and (3) tubule

formation. The agreement between pathologists using this

grading scheme has been measured and described in many

research articles. In one study, the agreement between six

pathologists was found to be poor to moderate for all three

features (κ= 0.64, 0.52, and 0.40 for tubule formation, mitotic

count, and nuclear grades, respectively).12 Another article,

published more recently, summarizes the findings of many

research studies that test pathologist concordance rates using

the NGS, including one that had 600 slide reviewers.13 The

findings of this article demonstrate further the variability in

slide review for grading, as well as the motivation for creating

new technology to address these issues.

There are many reasons why discordance in pathological

tissue interpretation exists. Consider the Ki-67 PI; variability

could exist due to many factors: subjectivity in detecting

Ki-67 positivity (determining whether a cell has significant

enough “brown staining” to be considered positive); the user-

specific selection, number, and location of the high-power

fields (HPFs) used for scoring; as well as the rough estimation

of the number of cells versus physical counting. Although

many recommendations have been made to standardize

Ki-67 scoring methods,15 including the number and location

of HPFs for scoring, these standards are continuously

evolving and do not address all of the concerns, especially

operator-dependent subjectivity.

In IHC studies, such as Her2/ER/PR, pathologists depend on

both the number (proportion) of cells that are positively

stained as well as the intensity of the stain. The intensity of

the stain (DAB chromogen) is visually estimated since

“darkness of stain” is associated with higher antigen

concentration. Therefore, unlike the determination of Ki-67

positivity, which is a binary response, determination of IHC

intensity values is much more difficult since the relative

amount of staining must be determined. It is practically

impossible to obtain objective measures of antigen

concentration and staining intensity using visual

examination. Moreover, since intensity scores are quantized

into usually four bins (0, 1+, 2+, 3), visual analysis cannot

yield a reliable and repeatable threshold to separate adjacent

scores. For example, what is the value of “darkness” that

distinguishes between a 3+ and a 2+ score?

Aside from IHC scoring metrics, grading systems that analyze

the morphology and structure of nuclei and tissue also suffer

from similar challenges. Consider the NGS, which uses three

morphological features: tubule formation (the percentage of

tubules or ducts that occupy the tumour area); nuclear grade

(shape, size, presence/lack of nucleoli, cellularity, and other

nuclear features); and mitotic counts (the number of cells

undergoing cell division per HPF). Estimation of the

percentage of tubule formation requires the accurate

calculation of the area of the tubules or ducts formed within

the tumour boundaries. Without any automated or

computer-assisted methods that count pixels, accurate

determination of the tubule formation percentage is not

feasible. Nuclear grading is based on a series of qualitative

descriptions that differentiate between high-grade and low-

grade cancers. Since the way qualitative descriptors correlate

with image interpretation and understanding is dependent

on the observer, nuclear grade is a difficult measure to

reproduce. For example, the term pleomorphic nuclei is not

quantitative and is therefore dependent on the interpretation

of the pathologist. Mitosis counting suffers from similar

challenges. Not only can mitoses be difficult to detect and

recognize, but counting them is time consuming.

Although only a few examples are highlighted, it is easy to see

that visual analysis creates variability in the estimation of

scales and grades. This effect is more pronounced in

laboratories that have limited access to subspecialists since a

lack of experience creates greater discordance. Additionally,


AUTOMATIC SCORING AND GRADING OF DIGITAL PATHOLOGY IMAGES

obtaining some scores is more laborious and time-consuming

than others, and this contributes to subjectivity (e.g.,

estimation of the number of cells versus actual counting).

Some of the main factors contributing to variability in slide

interpretation include the following:

• Determining antigen-concentration based on visual

perception of chromogen intensity

• Establishing antigen-positivity based on visual

perception of colour

• Visual and qualitative definitions of thresholds to

differentiate between intensity scores

• Choosing the most representative HPF for scoring and

grading according to image understanding and

experience

• Selecting the adequate number of HPFs based on

evolvingQA protocols

• Using qualitative descriptors to explain visual cues, such

as cell and tissue morphology

• Manual counting or “eye-balling” to estimate the number

of cells and objects

• Visual estimation of measurements, such as area, volume,

size, etc.

As these scales are critical in patient management and

treatment, new technologies are being sought to assist the

pathologist in order to increase the accuracy, efficiency,

reliability, and repeatability of scoring and grading systems.

The following section briefly discusses how algorithms can

help improve analytical quality.

Image Analysis SystemsFortunately, now that pathology

samples are being digitized and stored

in large databases, image analysis

solutions, or algorithms, can be utilized

to combat the challenges in slide

interpretation. Image analysis systems

are a series of software modules that

automatically perform some operations

on a digital image. The output is a

processed image that displays the

results of the algorithm visually (as an

overlay), or a series of metrics that

quantitatively describe what was

detected by the algorithm. Consider Figure 1, which displays

the workflow for an algorithm that automatically detects nuclei

in breast biopsies. The output is displayed as an overlay (red

outlines highlight the detected nuclei) and the table below

shows numerical values that describe the number of nuclei

detected and the average area of the nuclei in this HPF (µm2).

To design this algorithm, a series of mathematical

transformations and operations were implemented in

software and applied to the digital image. Since algorithms

are dependent on mathematical analysis, they produce robust,

objective, and quantitative measures of disease. Every time

this program is executed on the same tissue sample, the

answer will be the same, ensuring reliable and repeatable

answers. Since algorithms are implemented on computing

devices, they are efficient and can speed up some of the

quantitation tasks of the pathologist. Image analysis solutions

have the potential to create uniform grading and scoring

methods, and also to increase efficiency. These tools are not

made to replace the pathologist but, rather, are supposed to

be used as software-based measurement tools that assist

pathologists in their daily tasks. Using image analysis

algorithms, many clinical applications are possible. For

example, automatic algorithms can compute reproducible

Allred and H-scores for ER/PR and Her2/neu analysis. The

digital value, or intensity of a DAB chromogen, can be used

to quantify the intensity scores, and object detection can be

used to count antigen-positive nuclei or membranes for

proportion scoring.

Grading schemes would also greatly benefit from automated

approaches. The nuclei that are detected by segmentation

Figure 1. Image analysis system for a nuclei segmentation algorithm.


KHADEMI

algorithms, such as the one shown in Figure 1, can be fed into

a feature extraction and classification engine that outputs

quantities that describe the circularity, texture, size, and colour

of every nucleus in a robust, reliable manner. These metrics

can be used to derive the nuclear grade for a specific sample.

Tubule formulation has similar potential. An algorithm that

firstly segments the entire tumour is needed, and the area of

this tumour, measured by counting pixels, is recorded. Next,

white objects that are surrounded by a ring of nuclei are

detected, and their corresponding areas are measured. Tubule

formation percentage can then be quantitatively measured by

dividing the area of the tubules by the total tumour size.

Although these systems are complicated and challenging to

create, mitosis counting is a much more difficult problem. As

the cell undergoes division, the appearance, colour, size, and

shape of the cells vary significantly. Since image analysis

systems use mathematics to detect and quantify objects, such

systems require some sort of constancy in the underlying

model. If there is variability in all aspects of this problem, it

becomes extremely challenging. However, several efforts are

under way in the academic community, and moderate

progress has been made.

Quantification algorithms can be applied to the entire slide

or selected reproducible HPFs. Such technical requirements

can be coded into the program and can be designed to work

according to guidelines and protocols set out by the College

of American Pathologists (CAP). Moreover, if the appropriate

number of HPFs ideal for scoring or grading is not known,

image analysis tools can be used in research studies to

automatically find this correlation.

Another big advantage that image analysis solutions can offer

is in the area of research. Grading and scoring systems can be

developed by employing these methods on large data sets. The

results can be statistically analyzed and correlated with patient

outcome. The same goes for drug discovery and therapeutic

response analysis. Large patient cohorts can be managed and

quantified in a way that manual analysis cannot achieve.

ConclusionNow that pathology is going digital, clinical algorithms for

digital image analysis are just the beginning. In the future,

integrated diagnosis systems will be designed in which

algorithms automatically correlate pathology, radiology, and

molecular images and quantify the relationship between these

different yet complementary modalities. Patient health

information will also be included in such automated systems.

This “pathologist cockpit” will output a series of quantitative

metrics, or a personalized digital signature, that objectively

and uniquely describes the state of a patient in terms of

disease, prognosis, and likelihood of survival. Since multiple

sources of information will be included, a much broader

picture of the patient’s health will be presented. As will be seen

in the years to come, this will all tie into the big-data analytics

strategy that is now gaining momentum. These digital

signatures, image analysis tools, and big-data analytics hold

the key to unlocking the personalized medicine paradigm.

References1. McLellan B, McLeod R, Srigley J. Report of the Investigators of Surgical and

Pathology Issues at Three Essex County Hospitals: Hotel-Dieu Grace Hospital,

Leamington District Memorial Hospital and Windsor Regional Hospital.

Toronto (ON): Ministry of Health and Long-Term Care; 2010.

2. Ontario Medical Association. About QMP-LS. Toronto (ON): QMP-LS, 2013;

www.qmpls.org. Accessed March 2013.

3. Royal College of Pathologists. What Is Quality in Pathology? Report of a Meeting

to Discuss the Development of Laboratory Accreditation in the UK. London:

The College; 2009.

4. Novis DA, Konstantakos G. Reducing errors in the practices of pathology and

laboratory medicine: an industrial approach. Am J Clin Pathol 2006;126:S30–5.

5. Nakhleh RE. What is quality in surgical pathology? J Clin Pathol

2006;59(7):669–72.

6. Cross SS. Grading and scoring in histopathology. Histopathology 1998;33:99–

106.

7. Wludarski SC, Lopes LF, Berto TR, et al. HER2 testing in breast carcinoma: very

low concordance rate between reference and local laboratories in Brazil. Appl

Immunohistochem Mol Morphol 2011;19(2):112–8.

8. Bischoff FZ, Pham T, Wong KL, et al. Immunocytochemistry staining for

estrogen and progesterone receptor in circulating tumor cells: concordance

between primary and metastatic tumors. Cancer Res 2012;72(24 Suppl 3).

9. Kornaga EN, Klimowicz AC, Konno M, et al. Comparison of three commercial

ER/PR assays on a single clinical outcome series. Cancer Res 2012;72(24 Suppl 3).

10. Varga Z, Diebold J, Dommann-Scherrer C, et al. How reliable is Ki-67

immunohistochemistry in grade 2 breast carcinomas? A QA study of the Swiss

Working Group of Breast and Gynecopathologists. PLoS One 2012;7(5):e37379.

11. Garg K, Leitao MM Jr, Wynveen CA, et al. p53 overexpression in

morphologically ambiguous endometrial carcinomas correlates with adverse

clinical outcomes. Mod Pathol 2010;23:80–92.

12. Frierson HF Jr, Wolber RA, Berean KW, et al. Interobserver reproducibility of

the Nottingham modification of the Bloom and Richardson histologic grading

scheme for infiltrating ductal carcinoma. Am J Clin Pathol 1995;103(2):195–8.

13. Rakha EA, Reis-Filho JS, Baehner F, et al. Breast cancer prognostic classification

in the molecular era: the role of histological grade. Breast Cancer Res

2010:12:207.

14. Detre S, Saclani JG, Dowsett M. A “quickscore” method for

immunohistochemical semiquantitation: validation for oestrogen receptor in

breast carcinomas. J Clin Pathol 1995;48(9):876–8.

15. Dowsett M, Nielsen TO, A’Hern R, et al. Assessment of Ki67 in breast cancer:

recommendations from the International Ki67 in Breast Cancer working group.

J Natl Cancer Inst 2011;16;103(22):1656–64.


Implementation of CellaVision at a Consolidated Laboratory Medicine Program

Wendy Patterson, ART, is currently the senior technologist responsible for the day-to-day operations of the Malignant Hema-tology Laboratory at Juravinski Hospital. Teresa DiFrancesco, MLT, is the manager of Malignant Hematology, TransfusionMedicine, Special Coagulation and Molecular Hematology at St. Joseph’s Healthcare. Both are members of the HamiltonRegional Laboratory Medicine Program, in Hamilton, Ontario. Correspondence may be directed to [email protected] article has been peer reviewed.Competing interests: None declared

Wendy Patterson, ART, Teresa DiFrancesco, MLT

ABSTRACTThe Hamilton Regional Laboratory Medicine Program (HRLMP) is a regional laboratory

program providing laboratory services to four acute care and two urgent care sites. The HRLMP

consists of five clinical laboratories in two organizations, Hamilton Health Sciences and

St. Joseph’s Healthcare, located in Hamilton, Ontario. Consolidation of laboratory services

across the HRLMP is a primary strategic goal for the program. CellaVision provides automated

digital cell morphology and differentials where cells are automatically located on a stained

peripheral blood smear; cells are pre-classified, stored, and digitally viewed by a technologist.

Cell classification is confirmed or, if required, re-classified by a technologist. The HRLMP

implemented this innovative technology to allow for consolidation of morphology and

differentials to a centralized laboratory; to facilitate the advancement in morphology teaching

to medical laboratory technologists (MLTs), medical students, residents, and fellows; to help

address the shortage of experienced MLT morphologists; to provide standardization of practices

and competency assessment across the HRLMP; and to allow for timely access to results across

the HRLMP, with the goal to improve patient care and decrease patient risk.

RÉSUMÉ Le Hamilton Regional Laboratory Medicine Program (HRLMP) est un programme régional

de médecine de laboratoire qui fournit des services de laboratoire à quatre centres de soins actifs

et à deux centres de soins urgents. Le HRLMP comprend cinq laboratoires cliniques provenant

de deux organismes : le Hamilton Health Sciences (association des sciences de la santé de

Hamilton) et le St Joseph’s Healthcare (centre de soins de santé Saint-Joseph) de Hamilton,

Ontario. Le regroupement des services de laboratoire de l’ensemble du HRLMP est l’un des

principaux objectifs stratégiques du programme. Le système CellaVision effectue des analyses

numériques automatisées de morphologie cellulaire et de numération globulaire, dans lesquelles

les cellules sont repérées automatiquement sur un spécimen coloré d’un frottis de sang

périphérique. Les cellules sont préclassées, enregistrées, puis examinées numériquement par un

technologiste. La classification des cellules est alors confirmée ou, si nécessaire, refaite par le

technologiste. Le HRLMP a mis en place cette technologie novatrice pour : permettre le

regroupement des analyses de morphologie cellulaire et de numération globulaire dans un

ORIGINAL ARTICLE

Automated digital imaging systems are now available forthe examination of peripheral blood smears.1CellaVision (CellaVision AB, Lund, Sweden) is an

automated system, a peripheral blood smear application

designed for the differential count of leukocytes,

characterization of red blood cell morphology, and platelet

estimation. The system automatically locates and presents

images of blood cells on peripheral blood smears. The

technologist identifies and verifies the suggested

classification of each cell according to cell type. The software

gives remote users, such as laboratory physicians and

clinicians, access to analyzed slides and the possibility to

reclassify cells and sign out slides from another location.

This article summarizes the implementation of CellaVision

at the Hamilton Regional Laboratory Medicine Program

(HRLMP), a large regional laboratory medicine program

consisting of five clinical laboratories, located in Hamilton,

Ontario. These clinical laboratories serve a varied patient

population, including trauma patients, surgical patients,

patients from all the medical subspecialties, and

pediatric/newborn patients. In this era of limited resources,

including declining numbers of laboratory technologists

experienced in peripheral blood morphology, consolidation

of laboratory activities is a logical choice.2

The project for implementation of CellaVision involved the

model definition, method validation, standardization of

practices across the program, training and competency

assessment, and project evaluation.

Methods and ResultsStep 1: Model Design, Equipment, and StaffingBefore the project could begin, a model needed to be defined

that would support the HRLMP’s strategic goal of

consolidating laboratory testing. The implementation would

see all peripheral blood smears performed in a central

laboratory only. The laboratory information system (LIS)

was a key component of the project. A centralized server was

configured with the CellaVision to ensure long- and short-

term storage of the images and patient information. A

bidirectional interface was developed, and the standardized

testing parameters were incorporated into the LIS, Meditech

(Woburn, Massachusetts). Any slide run on an HRLMP

CellaVision can be viewed at any remote viewing station in

the city that has active software installed. The model

demonstrated that this would be accomplished

predominately using remote access software (Figure 1).

Workload was assessed in all core laboratories to include

staff requirements with the movement of the morphology

to a central laboratory. The CellaVision equipment was

installed at all five core laboratory sites. Each instrument was

evaluated to ensure its operation and performance were

acceptable. New automated stainers were purchased and

installed in each core laboratory to ensure standardization

of smear staining. Turnaround times were captured from


PATTERSON AND DIFRANCESCO

laboratoire centralisé; favoriser l’avancement de l’enseignement de la morphologie chez les

technologistes de laboratoire médical (TLM), les étudiants en médecine, les résidents et les

boursiers; aider à contrer la rareté des TLM morphologistes expérimentés; standardiser les

pratiques et l’évaluation des compétences dans l’ensemble du HRLMP; et permettre un accès

rapide aux résultats dans tout le HRLMP, et ce,dans le but d’améliorer les soins aux patients et

de diminuer les risques encourus par ceux-ci.

bidirectional interface

bidirectional interface

Figure 1. Model for the implementation of CellaVision.


IMPLEMENTATION OF CELLAVISION

the laboratory information system pre- and post-

implementation. Peak times were identified at all five

laboratory sites, as well as stat and routine orders. These data

were used to provide optimal staff coverage in the central

laboratory. Due to variation in patient populations at the

different hospital sites, representatives from each worked

together to standardize slide-making criteria, slide

preparation, staining, and standard operating procedures.

Transportation requirements were determined to ensure the

timely delivery of any slides that were not suitable for

CellaVision and that needed to be reviewed manually.

Step 2: Verification and “Going Live”The verification of the CellaVision included 300 slides from

patients with varying hemoglobin concentrations and

leukocyte and platelet counts. Both pediatric and adult

samples were used. Various malignancies, such as acute

leukemia, essential thrombocythemia, and lymphoma were

also included. The validation was performed by medical

laboratory technicians (MLTs) with varying morphological

experience; the slides were blinded. Two manual differentials

were compared to the CellaVision results. Precision of the

instruments was also determined by making 10 slides on the

same patient and analyzing them on each of the CellaVision

units (Table 1).

The project began in April 2008 and was successfully

implemented in June 2009. The verification demonstrated

correlation between CellaVision and our existing manual

morphology and differential methods. The training

component included a gap assessment, technical training on

CellaVision for technologists and physicians, and

appropriate smear-making techniques for both MLTs and

medical laboratory assistants (MLAs) at all sites. When this

was completed, a competency assessment of all training

domains was completed. The project also included a detailed

assessment of current smear-making criteria in the HRLMP

to standardize referral practices for morphology and ensure

the clinical requirements of the morphology were met.

“Go-live” dates for each of the five laboratory sites were

staggered over an 18-month period to allow the

technologists at the central site to adapt to using the

CellaVision and the increased workload associated with the

consolidation of morphology to a single laboratory.

Step 3: Follow-UpTurnaround times for reporting morphology on peripheral

blood smears have improved (Table 2). This is likely due to the

technologists gaining experience and confidence with the

preparation of smears, viewing CellaVision images, and using

the remote software.

Smear-making criteria were revisited 1 year after the

implementation of CellaVision. The guidelines of the

International Society of Laboratory Hematology “Suggested

Criteria for Action Following Automated CBC and WBC

Differential Analysis” were used in conjunction with physician

input in an effort to further reduce the number of smears

prepared and sent for morphology assessment.3 A 15%

decrease in workload was realized after the implementation of

the new HRLMP guidelines. Another example of a local

adaptation of peripheral blood review guidelines resulting in

increased efficiency has recently been published.4

Four hundred peripheral blood smears are examined in the

HRLMP centralized laboratory daily using CellaVision remote

Table 1. Precision Results for CellaVision% Coefficient of Variation

Cell Type Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Overall Neutrophil – band 58.7 43.7 62.5 45.4 58.4 49.8 53.1Neutrophil – segmented 9.3 9.4 13.0 12.6 17.0 10.3 11.9Lymphocytes 12.1 9.6 15.5 13.3 24.2 12.4 14.5Monocytes 26.5 25.1 41.6 26.7 26.2 25.7 28.6Eosinophils 70.8 43.0 41.1 55.5 58.8 53.7 53.8Basophils 47.2 47.1 47.3 64.3 43.3 53.2 50.4

Table 2. Leukocyte Differential Turnaround Times Pre- andPost-implementation of CellaVision Pre-implementation Post-implementationNovember 2008 September 2009 January 20104.6 hours 2.7 hours 1.8 hours


PATTERSON AND DIFRANCESCO

access software. On average, seven slides per day need to be

examined manually under a light transmission microscope.

Most of these manual reviews are performed on samples from

neonates who have hemoglobin levels >200 g/dL.

ConclusionsThe successful implementation of CellaVision in the

HRLMP has allowed for the consolidation of peripheral

blood morphology at a central laboratory. This project has

streamlined the morphology ordering practices, provided a

consistent reporting system, and allowed the HRLMP to

build a centre of excellence for red blood cell morphology

and white blood cell differentials to ensure quality patient

care.

AcknowledgementsThe authors would like to thank Dr. Ruth Padmore for her

assistance in the draft stages of this manuscript.

References1. Yu H, OK CY, Hesse A, et al. Evaluation of an automated digital imaging

system, Nextslide Digital Review Network, for examination of peripheral

blood smears. Arch Pathol Lab Med 2012;136(6):660–7.

2. Rollins-Raval MA, Raval JS, Contis L. Experience with CellaVision DM96 for

peripheral blood differentials in a large multi-center academic hospital system.

J Pathol Inform 2012;3:29.

3. Barnes PW, McFadden SL, Machin SJ, Simson E. The international consensus

group for hematology review: suggested criteria for action following

automated CBC and WBC differential analysis. Lab Hematol 2005;11:83–90.

4. Kim SJ, Kim Y, Shin S, et al. Comparison study of the rates of manual

peripheral blood smear review from 3 automated hematology analyzers,

Unicel DxH 800, ADVIA 2120i, and EX2100, using international consensus

group guidelines. Arch Pathol Lab Med 2012;136(11):1408–13.

DISPLAY CLASSIFIED

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“Progressing” Multicystic Mesothelioma of the Liver

Mara Caragea, MD, Christopher J. Howlett, MD, PhD, FRCP(C), Jeremy R. Parfitt, MD, FRCPC, and Subrata Chakrabarti, MD,PhD, FRCP(C), are members of the Department of Pathology, University of Western Ontario, in London, Ontario. PamelaSmith, MD, BSc, MBChB, FRCP(C), is a member of the Department of Pathology, Windsor Regional Hospital, in Windsor, On-tario. Correspondence may be directed to [email protected] article has been peer reviewed.Competing interests: none declared

Mara Caragea, MD, Pamela Smith, MD, BSc, MBChB, FRCP(C), Christopher J. Howlett, MD, PhD, FRCP(C), Jeremy R. Parfitt, MD, FRCPC, Subrata Chakrabarti, MD, PhD, FRCP(C)

ORIGINAL ARTICLE

ABSTRACTBenign multicystic mesothelioma (BMM) arising in the liver is an extremely rare tumour usually

affecting young women, and malignant transformation in a BMM has only been reported twice.

In this article, the authors present a case of an unusual BMM arising in the liver and showing

evidence of progression to a malignant mesothelioma. A 44-year-old woman presented with a

large palpable abdominal mass. An ultrasound examination revealed a 10 cm complex cystic

mass in the left lobe of the liver, and a partial liver resection was performed. Grossly, the tumour

was composed of multiple cysts containing gelatinous and clear fluid. Microscopically,

microcysts and tubular spaces lined by flat or cuboidal cells were set in an edematous and

collagenous stroma with numerous large atypical cells with hyperchromatic, and frequently

multiple, nuclei. Scattered atypical mitotic figures were noted. Immunohistochemical studies

confirmed the mesothelial origin of both the cells lining the cysts and the atypical stromal cells.

BMM undergoing malignant transformation is an extremely rare event that, to the best of our

knowledge, has been described only twice. The presence of cytological atypia and atypical

mitoses in a BMM raises the possibility of progression to a malignant mesothelioma.

RÉSUMÉ Le mésothéliome multikystique bénin naissant au foie est une tumeur extrêmement rare

touchant surtout la femme jeune, et la transformation maligne de cette tumeur n’a été rapportée

qu’à deux reprises. L’article aborde le cas d’un mésothéliome multikystique bénin inhabituel

au foie évoluant selon toute apparence vers la malignité. Il s’agit d’une femme de 44 ans affligée

d’une volumineuse masse abdominale palpable. L’échographie révèle une formation kystique

complexe de 10 cm dans le lobe gauche du foie. La patiente subit une résection partielle du foie.

Sous l’angle macroscopique, il appert que la tumeur se compose de plusieurs kystes renfermant

du liquide gélatineux et clair. La microscopie met en évidence de minuscules kystes et des

structures tubulaires intercalaires tapissés de cellules planes ou cubiques, assemblés dans un

stroma de collagène oedématié en compagnie de nombreuses grosses cellules atypiques dotées

de plusieurs noyaux hyperchromatiques pour la plupart, ainsi que des éléments mitotiques

atypiques épars. L’analyse immunohistochimique confirme l’origine mésothéliale des cellules

tapissant les kystes et des cellules atypiques du stroma. Le mésothéliome multikystique bénin


CARAGEA ET AL.

Benign multicystic mesothelioma (BMM) is a benignmesothelial neoplasm most commonly encountered infemales of reproductive age. Although approximately 130

cases have been described in the literature, only on rare

occasions have these lesions arisen primarily in the liver.1,2

The origin and the natural progression of BMM are not well

established. Controversy also exists regarding the etiology

of these lesions. Some authors consider them reactive, while

others describe them as neoplastic.3 However, it is well

accepted that BMMs are associated with a high recurrence

rate.

BMMs are composed of thin-walled multilocular cysts lined

by bland cuboidal or flat epithelial cells with a hobnailed

appearance.1 These cells are mesothelial in nature, as

indicated by immunohistochemical and electron

microscopic studies. The cysts are separated by fibrous septa

containing acute and chronic inflammatory cells. Areas of

hemorrhage and vascular congestion are often seen in the

septa. Squamous metaplasia and adenomatoid changes may

also occur.3

Malignant transformation is exceedingly rare, with only two

cases reported to date.4,5 In 1985, DeStephano et al. reported

a cystic liver lesion in a 6-month-old infant.4 Examination

of that lesion showed a BMM coexisting with a malignant

mesothelioma. The patient succumbed to the disease 11

months later. Several years later, González-Moreno et al.

provided new information on the natural history of this

disease by reporting a case of malignant transformation in

a long-standing BMM.5 That article reported a 36-year-old

woman who presented with a malignant mesothelioma

arising in a benign cystic mesothelioma 10 years after the

initial diagnosis.

Case ReportA 44-year-old woman presented with an 8-month history

of abdominal pain and difficulty breathing. Physical

examination revealed a palpable abdominal mass extending

from the right upper quadrant across the midline. Her past

medical history was unremarkable. An abdominal

ultrasonography revealed a complex cystic mass measuring

15 × 15 × 8 cm in the left lobe of the liver. The initial

differential diagnosis included a primary cystic neoplasm

versus an infectious or inflammatory process. A second

abdominal ultrasonography and hepatic Doppler study a

few months later showed a 20 × 11 × 9 cm mass. A

subsequent magnetic resonance imaging (MRI) revealed a

predominately cystic mass with areas of solid enhancement

abutting the left lobe of the liver and displacing the stomach

inferiorly. An incidental hemangioma was present in

segment 6 of the liver. It was felt that biliary cystadenoma

or adenocarcinoma, undifferentiated embryonal cell

sarcoma, and metastatic disease were diagnostic possibilities;

a partial liver resection was performed. The operative report

described a lobular, cystic lesion measuring approximately

25 × 20 cm that occupied almost the entire left lateral lobe.

Note was made of an incidental hemangioma present in

segment 6.

On gross pathological examination, a multicystic, lobular

lesion measuring 20.1 × 18.7 × 8.5 cm and containinggelatinous and clear fluid replaced almost the entire left lobe

of the liver. The cysts ranged in size from 1.0 to 7.5 cm in

diameter. The lesion was well circumscribed and away from

the resection margin. The adjacent liver parenchyma was

unremarkable. Microscopically, multiple cysts and tubular

spaces lined by flat or cuboidal cells were set in an

alternating edematous and collagenous stroma (Figure 1).

Focal cytological atypia was present in these cells. Within

the stroma there were numerous large, atypical cells with

hyperchromatic nuclei. Frequent multinucleation and

scattered atypical mitotic figures were noted (Figure 2).

Areas of hemorrhage and fibrin exudation were also present

throughout the lesion.

évoluant vers la malignité est une entité extrêmement rare, un phénomène qui n’a été constaté

qu’à deux reprises pour autant que nous sachions. L’atypie cytologique et mitotique à l’examen

du mésothéliome multikystique bénin serait peut-être une indication de l’évolution vers un

mésothéliome malin.


“PROGRESSING” MULTICYSTIC MESOTHELIOMA OF THE LIVER

Appropriately controlled immunohistochemical studies

were performed. Nuclear and cytoplasmic calretinin

expression was noted in both the cells lining the cysts and

the atypical stromal cells (Figure 3). Focal nuclear expression

of WT-1 and D2-40 immunoreactivity confirmed

mesothelial differentiation of both the epithelial and stromal

cells. The cyst lining cells, and occasional stromal cells, were

positive for CK5/6, CK7, HBME, CAM 5.2, CKAE1/AE3,

and vimentin (Figure 3). All neoplastic cells were negative

for MOC31, thrombomodulin, HepPAR1, BRST2, CK20,

Alk1, inhibin, CD31, CD34, factor VIII, CD68, ER, PR, and

TTF-1. Colloidal iron and the PAS reagent after diastase

treatment were positive in the epithelial cells lining the cysts.

The patient is currently asymptomatic, 26 months after

resection, and has no evidence of recurrence.

A B

Figure 1. A and B, The lesion is composed of cystic spaces lined by flattened mesothelial cells set in edematous and collagenous stroma.(Hematoxylin and eosin)

Figure 2. Numerous large atypical cells with hyperchromatic, andfrequently multiple, nuclei (A), as well as atypical mitotic figures(B and C) are seen. (Hematoxylin and eosin)

A

C

B


CARAGEA ET AL.

DiscussionPeritoneal BMM is a neoplastic process commonly seen in

young women. BMM primarily arising within the liver is

exceedingly rare.1,2 More often, liver involvement occurs

secondary to peritoneal disease. The pathogenesis of this

lesion remains controversial. While some authors favour this

as a reactive proliferation, supported by the frequent

association with previous surgical procedures, inflammation,

or endometriosis, others favour a neoplastic etiology.3 These

lesions show progressive growth when left untreated and have

a tendency for local recurrence, mainly as a result of

incomplete excision. However, little is known about the

potential for malignant progression of BMM.

Malignant transformation of BMM is an extremely rare

event.4,5 In the case reported by DeStephano et al., areas of

BMM and malignant epithelioid mesothelioma coexisted

within the same lesion in a 6-month-old infant.4 The patient

underwent partial liver resection but succumbed to the

disease 11 months later. The post-mortem examination

revealed residual liver disease and diffuse parietal and

visceral peritoneal spread. No lymph node metastases were

present. González-Moreno et al. reported a malignant

mesothelioma arising within a BMM 10 years after the initial

diagnosis.5 The disease recurred as a malignant

mesothelioma on a background of classical BMM. The

malignant area was composed of cysts with thick walls

infiltrated by tubules and nests of mesothelial cells with mild

to moderate atypia and no or low mitotic activity. This

tumour had a papillary and invasive growth pattern with

diffuse involvement of the peritoneal surfaces of multiple

organs, as well as numerous lymph node metastases.

The differential diagnosis in the current case includes

vascular neoplasms, such as lymphangioma, and serous

cystadenoma, which were excluded using

immunohistochemical markers. The histological and

immunohistochemical features are consistent with a BMM,

A B

C D

Figure 3. Immunoperoxidase studies reveal nuclear and cytoplasmic expression of calretinin (A and B). HBME (C) and CK5/6 (D) are diffuselyexpressed in the mesothelial cells lining the cysts and show patchy expression in the atypical stromal cells.


“PROGRESSING” MULTICYSTIC MESOTHELIOMA OF THE LIVER

but the presence of scattered, highly atypical stromal cells

with atypical mitoses was concerning and a malignant

mesothelioma could not be ruled out. We propose that these

features are indicative of progression toward a malignant

mesothelioma. This would be consistent with the

immunohistochemical findings, as the sarcomatoid

component of biphasic malignant mesothelioma is known

to lose expression of mesothelial markers.6

The two cases of malignant transformation previously

described and the current case indicate that the designation

“benign” multicystic mesothelioma may be inappropriate;

this neoplasm is best considered a multicystic mesothelial

neoplasm of uncertain malignant potential. Since

malignancy cannot be ruled out, close clinical follow-up is

mandatory.

References1. Flemming P, Becker T, Klempnauer J, et al. Benign cystic mesothelioma of the

liver. Am J Surg Pathol 2002;26(11):1523–7.

2. Di Blasi A, Boscaino A, De Dominicis G, et al. Multicystic mesothelioma of

the liver with secondary involvement of peritoneum and inguinal region. Int

J Surg Pathol 2004;12(1):87–91.

3. Weiss SW, Tavassoli FA. Multicystic mesothelioma. An analysis of pathologic

findings and biologic behavior in 37 cases. Am J Surg Pathol 1988;12:737–46.

4. DeStephano DB, Wesley JR, Heidelberger KP, et al. Primitive cystic hepatic

neoplasm of infancy with mesothelial differentiation: report of a case. Pediatr

Pathol 1985;4:291–302.

5. Gonzáles-Moreno S, Yan H, Alcorn KW, Sugarbaker PH. Malignant

transformation of “benign” cystic mesothelioma of the peritoneum. J Surg

Oncol 2002;79:243–51.

6. Marchevsky AM. Application of immunohistochemistry to the diagnosis of

malignant mesothelioma. Arch Pathol Lab Med 2008;132:397–401.


Laboratory Utilization Trends: Past and Future

Megan-Joy Rockey, BHSc, Christopher Naugler, MD, Davinder Sidhu, LLB, MD, are with Calgary Laboratory Services. Christo-pher Naugler and

canaadian jtouhrnal ofolog y volume 5, issue 2 summer 2013 · 2020. 7. 6. · digital pathology...

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