The Pathology Imaging Engine Histology Workflow with WSI

1. HARVARDMEDICAL SCHOOL Pathology Visions 2010 October 25, 2010 San Diego, CA The Pathology Imaging Engine Histology Workflow with WSI John Gilbertson MD Associate Chief of Pathology Director of Pathology Informatics Massachusetts General Hospital Associate Professor of Pathology Harvard Medical School

2. I have no personal financial relationship with any company* • However, I am Associated Chief of Pathology and Director of Pathology Informatics in a Department that does significant research and co-development with industry • Names that we work with and I might mention today include Kurabo, Sony, Olympus, 3D Histech, Corista, Bioimagene, Hamamatsu and Sunquest* • I am overall PI on a collaboration agreement with Philips • All conflicts are disclosed and compliant with Partners and Harvard ethics rules Pathology Visions 2010

3. Today’s Talk: • The potential of WSI and Digital Pathology • The importance of histology (slide quality and variance) in WSI • The importance of maintaining histology throughput when implementing large scale, clinical WSI • The use of process mapping, modeling and computer simulation in the planning for WSI in the histology lab Pathology Visions 2010

4. Pathology Visions 2010

5. When pathology is able to digitize all of its slides, automatically, rapidly and with high fidelity, it will be able to apply computational power and network connectivity to study of microscopic morphology and the practice of anatomic pathology • Computational power and network connectivity are the driving forces of change in the modern world • They have changed every industry they have touched: they should change pathology Pathology Visions 2010

6. WSI is an enabling technology for “putting Pathology on the right side of Moore’s Law” Exponential decrease in computational cost – for decades Pathology Visions 2010

7. What if the rest of the medicine is taking advantage of growing computational power and network connectivity and pathology isn’t? … It will not end well • Not going “digital” is not an option Pathology Visions 2010

8. But Pathology Visions 2010

9. Digital pathology isn’t really digital… There still is the slide … …and the stain Stained slides are the subject of the WSI process… …but they have been an unwilling subject Pathology Visions 2010

10. They are designed to be looked at by eye under the microscope They were never designed to be imaged Pathology Visions 2010

11. Slides: for your eyes only • Many of them • Rapidly • By hand Pathology Visions 2010

12. In most laboratories, the glass slide has vary high variance Pathology Visions 2010

13. Machines hate this • Things that were never a problem for the human hand and pathologist’s eye were problems for the robotic actuator and the digital camera Pathology Visions 2010

14. Pathology Visions 2010

15. Slides are active participants in the digitization process Pathology Visions 2010

16. Slide variance is a major factor in imaging failures and imaging speed Pathology Visions 2010

17. When robots work with robots… Better slides  Images Faster image capture speed Archive Pathology Imaging Engine Interpretation Processing & Embedding Cutting & Adhesion Staining Optics & Digitization Image Analysis Grossing Workflow and Dataflow Infrastructure (LIS) • Lower variances – faster focus • Lower variance – fewer failures (unacceptable focus, tissue finding or mechanical failure) Pathology Visions 2010

18. Pathology Visions 2010

19. High slide quality, low variance Yagi et al Pathology Visions 2010

20. Yagi et al Pathology Visions 2010

21. Yagi et al Pathology Visions 2010

22. Quantitative QA: Better images  Better Slides Archive Pathology Imaging Engine Interpretation Processing & Embedding Cutting & Adhesion Staining Optics & Digitization Image Analysis Grossing Workflow and Dataflow Infrastructure (LIS) Bautista PA, Yagi Y Detection of tissue folds in whole slide images. Conf Proc IEEE Eng Med Biol Soc. 2009:3669-72. Pathology Visions 2010

23. There is a trend to incorporate robots and quantitative, Imaging-based QA at every step of the histology process This is great for imaging, histology and anatomic pathology Pathology Visions 2010

24. Throughput Pathology Visions 2010

25. We still have to push a lot of slide through histology – fast Digitization might make the entire process (gross – histology – signout) faster…. … large scale WSI will not be implemented if it significantly slows histology throughput Digitization adds steps (image, check, redo) Pathology Visions 2010

26. Anatomic Pathology Workflow • We have a real interest in AP productivity • Our pathologist’s time is the more valuable resource we have • This is true world wide • Potential Market is increasing… • Case complexity is increasing… • Need for documentation is increasing… • Research opportunities are increasing… • Pathology satisfaction is declining… • High turnover in Histology Labs • Aging infrastructure Pathology Visions 2010

27. Anatomic Pathology Workflow • We have a specific interest in AP productivity • Sunquest collaboration requires the implementation of a new AP LIS • Axiom: Bad workflow eats good software for lunch • Axiom: Don’t prop up bad workflow with new software • All these things give us a very good excuse to examine our workflow (and infrastructure) Pathology Visions 2010

28. Infrastructure to study/improve productivity • Green belts, analysts, techs and engineers (Lean, six sigma, FMEA) • Mapping: What is our baseline workflow’ • Modeling: Include rates, numbers, times (manually initially) • Populated Models: Asset tracking (2011), routing and station protocols (process steps) (2012) • Process Laboratory: “A laboratory to study the laboratory” • Simulation Software: “What if we make a change” • Clinical Laboratory: “Beta Testing” • Compare: Scientifically compare the pre- and post states Pathology Visions 2010

29. Current findings Mapping and manual modeling • ………………….! • 29 major workflows • 1000+ hand offs • Major Variation (interruptions, etc) • Cultural issues Pathology Visions 2010

30. Simulation • We began computer simulation of workflow about 5 months ago • Commercial simulation software developed for production lines (Lean, Six Sigma and FMEA analysis) • We will eventually use of asset tracking data (2011) • Decided to began using our current process maps and observations (time studies, red card studies, etc) • Our first “test” example, was what would happen if we decided to image all of slides prior to sending the slides to the pathologist Pathology Visions 2010

31. Simulation A story: • ~ 4.5 months ago… • ~ 3 weeks… • ~ 2.5 weeks ago… • Developing a simulation of laboratory that does 300 cases and 2500 slides a day, 1200 part types, 29 major processes (and wide variations in each process), 40+ people… is hard • ~ 1.5 weeks ago… • ~ 4 days ago… Pathology Visions 2010

32. Simulation • Data was not bad, it was impossible to understand on PPT • Data was not perfect: a lot of excessive waiting time because of steps were modeled. • Key points: • Our normal case mix • Begin in the processor (grossing room is a black box) • One input of cases (there is nothing in the lab except this one surge of cases) • Keep our normal number of processing, imbedding, cutting, drying, staining, drying, reconciliation stations • Keep our normal time (and variances) at each station • This was not reality Pathology Visions 2010

33. Simulation • Changed size of batches between stations and redo rate. • Run the simulation for 0, 1, 2, 3 scanners • Scanners imaged at 1-3 minute per slide rtr, • Image QA time 0 – 10 minutes • Image redo rate 0 – 10% Pathology Visions 2010

34. Simulation Results - Not based in reality, but useful… • Tidal Effect” (traffic jams) but the simulation did not “stop” • Batching drives throughput rate – especially in staining/drying • Batches of five slides each, allowed two scanners to image 400 slides (~ 600 – 800 minutes of work) while only adding 19 minutes to the run (redo rate = 0, QA time = 0) • Case size drives throughput rate • Imaging QA time is a major impact throughput rate (scan time + QA time = ) • Imaging redo kills throughput rate especially at rates above 5% or with large cases • Elimination of batching, case reconciliation, low QA time and minimum imaging failures are key Pathology Visions 2010

35. Conclusions • Digital slides are potentially powerful tools for pathology. Its gets us on the right side of Moore’s Law • Pathology imaging begins in Histology, it requires a stained slide • Slide quality impacts image quality and scanner specification: High quality, low variance slide result in high quality, high speed images • For large scale clinical WSI to be implemented, WSI must not significantly impact the distribution of slides to the pathologist • Preliminary simulations at MGH indicate that both workflow changes (especially minimizing batches) and improved image quality (decreased QA time and redos) are important parameters in maintaining high histology throughput with WSI • Computer simulations seems useful in evaluating a large number of process in pathology – especially in conjunction with asset tracking Pathology Visions 2010

36. Conclusions • We will be publishing our data so that you can make your own decisions… Pathology Visions 2010