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Digital Pathology: the Dutch Perspective

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Digital Pathology: the Dutch Perspective Digital pathology: The Dutch perspective Paul J van Diest, MD, PhD Professor and Head, Department of Pathology University Medical Center Utrecht PO Box 85500, 3508 GA Utrecht The Netherlands [email protected] Professor of Oncology Johns Hopkins Oncology Center Baltimore, MD, USA Notice of Faculty Disclosure In accordance with ACCME guidelines, any individual in a position to influence and/or control the content of this ASCP CME activity has disclosed all relevant financial relationships within the past 12 months with commercial interests that provide products and/or services related to the content of this CME activity. The individual below has responded that he/she has no relevant financial relationship(s) with commercial interest(s) to disclose: Paul J. van Diest, MD, PhD Disclosures • member of advisory boards of Roche, Sectra, Philips (unpaid) • client of Hamamatsu, Sectra, Visiopharm, Leica, Aurora, Philips (paying heavily) • test scanners from Philips and Roche (no contract, no fee) • joint research projects with Philips UMCU Why The Netherlands? • “bunch-a-crazy dudes” • innovation is our middle name • not bound by too many rules and EU-FDA • strong believe in advantages in digital pathology Advantages of digital pathology • no more case assembly in the lab efficacy • quicker diagnostics efficacy • easy and more accurate measurements and counting patient safety/efficacy • better overview of tissue present patient safety • flagging missing slides patient safety • tracking viewing patient safety • automatic linking of images/reporting/LMS patient safety • MDM: quicker preparation, anywhere, show images efficacy • easier making annotations efficacy • easier taking snapshots efficacy • no more slide searching efficacy • no more archive digging efficacy • multiple observer viewing efficacy • quicker and easier consultation patient safety/efficacy • quicker revision efficacy • integration of image analysis patient safety • remote digital diagnostics (including consultation) patient safety/efficacy • integration with radiology patient safety • integration of images in EPF clinician satisfaction • allows working paperless environmental • better ergonomics pathologist satisfaction • teaching is easier and better appreciated student satisfaction Workspace flexibility • Diagnostics at home, on the beach, at conferences • Less office space needed • Remote frozen section diagnostics Pittsburg UPMC Positions open for residents in pathology: The medical specialty where you no longer need to get out of bed The Dutch digital pathology landscape Leiden Hengelo Rotterdam Nijmegen Heerlen The Dutch perspective • 2 labs with full digital diagnostics workflow – UMC Utrecht – Labpon Hengelo • 1 lab with partial digital diagnostics workflow – Heerlen • 1 rogue pathologist working digitally • 2 consortia with a digital network – Den Bosch/Nieuwegein/Arnhem – Alkmaar/Zaandam/Hoorn • 7 academic centers with scanners 16 labs with digital pathology infrastructure and a lot more working on it! The Dutch perspective Vendors • Philips • Sectra/Hamamatsu • 3-D Histech Can we just do this? • No FDA in Europe • CE-IVD suffices • Internal validation required • No professional guidelines (CAP) UMCU digital diagnostics validation 1. Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method. PLoS One. 2016 Aug 16;11(8):e0161286. 2. Validity of whole slide images for scoring HER2 chromogenic in situ hybridisation in breast cancer. J Clin Pathol. 2016 May 9. 3. Improved quality of patient care through routine second review of histopathology specimens prior to multidisciplinary meetings. J Clin Pathol. 2016 Oct;69(10):866-71. 4. Whole slide images for primary diagnostics of urinary system pathology: a feasibility study. J Renal Inj Prev. 2014 Dec 1;3(4):91-6. 5. Evaluation of mitotic activity index in breast cancer using whole slide digital images. PLoS One. 2013 Dec 30;8(12):e82576. 6. Whole slide images for primary diagnostics of paediatric pathology specimens: a feasibility study. J Clin Pathol. 2013 Mar;66(3):218-23. 7. Whole slide images as a platform for initial diagnostics in histopathology in a medium-sized routine laboratory. J Clin Pathol. 2012 Dec;65(12):1107-11. 8. Digital slide images for primary diagnostics in breast pathology: a feasibility study. Hum Pathol. 2012 Dec;43(12):2318-25. 9. Whole slide images for primary diagnostics in dermatopathology: a feasibility study. J Clin Pathol. 2012 Feb;65(2):152-8. 10. Whole slide images for primary diagnostics of gastrointestinal tract pathology: a feasibility study. Hum Pathol. 2012 May;43(5):702-7. • double workflow for 2 years Digital diagnostics validation Digital pathology diagnostics YES WE CAN!!!!!!!!!!!! Pathology Image Exchange (PIE) Growing digital pathology landscape Increasing need of digital lab2lab communication – Subspecialization within regional networks – Covering superspecialties between academic centers – Revisions – Consultations – Expert panels – Research – Teaching Subspecialization between regional labs • Hematopathology, soft tissue pathology, cytology Lab A Lab B Lab E Lab C Lab D Top expertise Dutch academic centers Covering areas between academic labs Academic A Academic Academic D B Academic C Revision When referring patient, reassess pathology material • Quality control • Reformulate results in local and up to date jargon • Compare to new material • Material for demo at multidisciplinary meetings • Further testing • Volume ~ 27.000 per year in The Netherlands Consultation Second opinion from collaegue in a difficult case • Patient stays at original hospital • Volume ~ 10.000 per year in The Netherlands Consultation by regional labs Lab A Lab H Lab B Lab G academic Lab C Lab F Lab D Lab E regional Consultation by regional labs Academic A Academic Academic D Lab A B Academic C national Consultation by regional clusters Lab A Lab B Lab E Lab C Lab D academic regional Consultation by regional clusters academic Lab A academic A B Lab B Lab E Lab C Lab D academic academic C national D Logistics revisions and consultations • Dig up slides from archive or desk pathologist • To administrative office Lab A • Print report • Wrap • To post office • From post office to department • Unwrap Lab B • Register • Assess • Make report Logistics revisions and consultations • Print report Lab B • Wrap report and slides • To post office • From post office to department • Unwrap • Report to pathologist Lab A • Append to report • Slides back to the archive Logistics revisions and consultations • Process takes 3-14 days (50% > 4 days) • Patient has to wait for definitive result and treatment • Stress, loss of productivity and quality of life • Optimal mail logistics: process 2 days • Digitally: same day? Logistics digital revisions and consultations • Scan slides • Upload to server Lab A • Message through PALGA to other lab • Log into server • Assess Lab B • Make report • Report to requesting pathologist through PALGA Done on the same day Panels • 6-15 pathologist and residents • Travel for many kilometers • Often during rush hour/traffic jams • Hugh loss of productivity • Frequency 1/month Research • Unlock biobanks • Revisions for clinical trials Teaching • Case mix differs • Sharing wiki of rare cases • Sharing wiki of classic cases Patient safety • Dutch national pathology archive • All labs connected since 1990 • Unique infrastructure for national pathology initiatives Revisions and consultations central solution message message Lab A Lab B image Log in upload Panels central solution message message Pathologists Pathologist A B Lab A C D image log in E upload F ….. Revisions and consultations decentral solution message message Lab A Lab B log in log in portal Panels decentral solution message message Pathologists Pathologist A B Lab A C D E Log in Log in F portal ….. Professional architecture Lab A Lab B IMS 17. Request images 18 Image Repository . View (Key /Recieve Images ) 1 . 2 (Repository / Source) Request images . Images 4. Upload images to PIE Consumer 12 . Show Images and Data 6. Done (Key) Uploader 5. Register image in PIE Index 13. Retrieve locations + Data Location (Key 3. Upload images ) 14. Image Done 15 7. 16 . Retrieve Medical Data . PIE Revieve Medical Data Medical Data (Index) 8 LIS . Instruct Kern U 10 . Register data in PIE Index LIS ( Key Medical ) Data - DPS 11. Send Request Lab2Lab 9. Send Request (End-To-End Encrypted) (Secured Network) Medical Kern U-DPS Data Kern U-DPS (+ Data Repository) Central vs. decentral solution Central Decentral Security difficult very difficult Storage capacity low? none Upload time high none Development tough very tough Initial costs high very high Running costs ?? >?? Hybrid solution? • Temporary? Central decentral • Permanent? Tender issues • Must haves – Speed – DICOM – Security – Uptime – Connections PALGA, LMS, UDPS/Sympathy etc – Storage Tender issues • Assets – Video? – Sound? – Image analysis? – Speech recognition? – MDM tool? – Mobile device functionality? Finances • One off costs – NVVP – PALGA – IKNL – SKMS • Running costs – IKNL – Pathology labs (savings!!!!) Future • Upscale to international? • Central storage? The Dutch image analysis landscape Leiden Hengelo Rotterdam Nijmegen Eindhoven Heerlen Dutch image analysis consortia • UMC Utrecht/Technical University Eindhoven – Nicholas Stathonikos/Paul van Diest – Mitko Veta/Josien Pluim Breast cancer grading • UMC Radboud/UMC Utrecht/Hengelo – Jeroen van der Laak – Quirine Manson/Paul van Diest – Alexi Baidoshvili
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