Objectives

1. Whole Slide Image Based Interpretation of Immunohistochemistry Stains in Challenging Prostate Needle Biopsies Jeffrey L Fine MD, Jonhan Ho MD, Yukako Yagi, Drazen Jukic MD PhD, John R Gilbertson MD, Sheldon I Bastacky MD, Dana M Grzybicki MD PhD, Leslie Anthony, Robb Wilson, and Anil V Parwani MD PhD

2. Objectives • Review whole slide image “landscape” • Present research project • Discuss implications arising from the study

3. Whole Slide Image (WSI) • Digital facsimile of an entire glass microscope slide that is viewed by “virtual microscopy” (VM) software • WSI are also known as “Virtual Slides” or “Digital Slides”

5. The Landscape: WSI Systems • Systems are currently self contained • Image acquisition, management, storage, and utilization (viewing and image analysis) • Bar code capabilities limited to “reading”

6. “Obvious” Current Trends • Decreasing cost for robots and storage • Increasing speed for robots • Raw capture speed • Better “shortcuts” • Involvement of traditional microscopy players • Olympus, Zeiss, Nikon

7. Nascent Trends • Vendor concern about “workflow” and “integration” • How to slip a robot into an existing APLIS and histology workflow • Digital pathology workstations • Monitors (how many and how large) • Display calibration

8. Clinically-Oriented Research:WSI “Clinical Evaluation Group” • Core affiliated group: • 4 pathologists; 1 fellow; study coordinator; data coordinators; imaging technicians; LIS personnel • Additional pathologists, depending upon study • Prior studies • Quality Assurance • Primary Diagnosis

9. Current Project • Goal: Validation of WSI technology for interpretation of immunohistochemistry (IHC) stains • Why? • UPMC has a centralized IHC laboratory that supports two academic hospitals • Electronic distribution (via WSI) could decrease turn-around time for IHC stains • Better patient care; better service to clinicians • Decreased healthcare cost (shorter length of stay?) • WSI could permit automated image analysis of IHC

10. Traditional workflow • Slides stained • Slides sorted and gathered • When a group of stains is complete they can be shipped to pathologist • Slides packed and shipped (courier) • Received slides are sorted (again) and distributed to pathologists

11. WSI workflow • Slides are stained • Stained slides are placed into a slide scanning robot which reads their bar codes and does the heavy lifting (naming of file; copying of file to server; etc.) • Pathologist views the slides directly over the internet • Glass slides catch up later (optional?)

12. Prostate Needle Biopsies • Availability at UPMC Shadyside • Small set of “usual” IHC stains • p63; cytokeratin 903; racemase • Typically signed out in an itemized fashion • Detailed information about each part or block • Very challenging IHC interpretation

13. Cytokeratin 903 “immuno stain” stains cytoplasm of basal cells

14. p63 “immuno stain” stains nuclei of basal cells (positive = noninvasive)

15. racemase (aka AMACR) “immuno stain” stains cytoplasm of glandular cells in prostate

16. Retrospective Study:Possible UPMC Environment • Stage I • Pathologist has glass H&E which requires IHC staining for definitive diagnosis • Stage II • Pathologist receives WSI of IHC stains and interprets them • Stage III • Glass IHC stains are eventually received and are checked by the pathologist • Consensus conferences

17. Study Design • 100 cases screened • 30 difficult foci found • Each study “case” represents one focus

18. Technology • High throughput WSI system • T2 (Aperio Technologies, Vista, CA, USA) • Viewing • Either WWW-based viewer or standalone viewer (both supplied by WSI vendor) • “Standard” desktop PCs and microscopes • Server • Nothing special (5 users and ~17 – 20 GB)

19. Data Collection • Stain by stain interpretation (stages 2 – 3) • Overall Diagnosis • Confidence in diagnosis • Time required to make diagnosis (roughly) • Complexity of case • Quality of each slide or image • Explanations for any defects or shortcomings, including network speed

20. Stain Interpretations • Positive • Negative • Can’t Tell (“?”) • Subcategories to help determine why the pathologist couldn’t intrepret the stain

21. Additional Data Collection • Consensus Diagnosis • Is mild disagreement OK (atypical vs. cancer) • How did this compare with original diagnosis • Any relevant features or notes about case • Image defects (de-focused areas; color reproduction; etc.) • Poor stain quality (not the image’s fault)

22. Results

23. Intra-observer Agreement(Stain Interpretation WSI vs Glass) • Five Pathologists • Average Intra-observer agreement • 80.6% (standard deviation 4.5%) • Range (75.7% - 86.0%)

24. Additional Results:Image Defects • Pre-existing QC procedure did not detect several defective images • Edge Defects • Rotation Defects

25. Edge Defect

26. Rescanned

27. Rotation H&E Immuno

28. H&E Immuno

31. Discussion

32. Validation • Does this study validate WSI for interpretation of IHC stains? • Pathologists agreed with themselves about 80% of the time • Need to find most common sources of disagreement and see if they can be addressed • It does highlight several points that need to be addressed prior to using WSI technology for real clinical applications

33. WSI Quality Control • Each WSI must be checked for common defects • This has to be automated eventually • All slides are not equal • IHC stains are susceptible to edge defects • Frozen section slides are hard to get focused • Image quality standards do not exist yet for WSI

34. Modification to WSI Process • Created a QC procedure (manual) • Includes solutions/fixes • Performed by technical support staff • Documentation of QC activities (aka QA) • Log files • Monitor image quality • Minimize sub-optimal or defective WSI that are “released” to pathologists

35. Workflow • Glass was felt to be faster • Current pathology systems do not accommodate WSI • Look up case in pathology system and click on available slides

38. Viewer Limitations(Most Systems) • Image navigation • (slow click and drag) • cannot rotate image easily (GI; skin; IHC stains) • Presentation speed is slow • (pixels are visible until image can load completely) • Lack of clinical data integration • (who’s slide is this?)

39. Study Flaws • Pathologist subjects • Informatics fellows; non-GU pathologist; GU-trained sub-specialists • Almost all pathologists were “informatics” pathologists • No standard display or VM software • 2 options for VM software • No “gold standard” for monitor/PC • Loose track of time

40. Future Work • Address flaws • Pathologist selection • Attention to software and computer used to participate in study • Other applications • Frozen Sections

41. Conclusions • This study provided experience in the attempted production of “clinical grade” pathology images • Experience has altered our QC procedures • Further tools are needed (automation, integration, etc.)

42. Conclusions • If validated (not yet), WSI technology could permit electronic distribution of IHC stains • Reduced turn around time could improve service and reduce healthcare cost • Centralized laboratories could support multiple hospitals or pathologist groups • Automated image analysis could be a future source of added value

43. Conclusions • WSI technology is entering a new phase • Machines/systems are adequate for small scale educational and research use • WSI systems are not yet capable of integration with existing pathology systems • This study (when published) can stimulate vendors and mainstream pathologists effectively transition to the next level

44. Acknowledgements • Rebecca Crowley MD • Michael Becich MD PhD • Russ Silowash • Jon Duboy