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 1.Slides stained 2.Slides sorted and gathered –When a group of stains is complete they can be shipped to pathologist 3.Slides packed and shipped (courier) 4.Received slides are sorted (again) and distributed to pathologists

11. WSI workflow 1.Slides are stained 2.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.) 3.Pathologist views the slides directly over the internet 4.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