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Digital pathology and biobanks

Explains what biobanks (tissuebank) are and how digital pathology (whole slide imaging) can add value to promote and increase their use

Digital pathology and biobanks

  1. 1. 8-1-2016 pag. 1 How digital pathology can add value to your biobank Yves Sucaet, PhD Diabetes Research Center CMI
  2. 2. 8-1-2016 pag. 2 Who am I? Education • 2000: Hogeschool Gent (BE) – BS Computer Sciences • 2001-2005: Troy State University (US) – Exchange program – MS Biological Sciences • 2005-2010: Iowa State University – PhD Bioinformatics & Computational Biology Professional • 2000-2001: Becton Dickinson • 2010-2013: HistoGeneX • Data Management • Bioinformatics • Section head • 2014-2016: VUB • Digital Pathology Manager
  3. 3. 8-1-2016 pag. 3 Presentation outline • Background – What is Whole Slide Imaging (WSI) – What is Digital Pathology • What is a biobank? • Adding value to your biobank – By adding slide imaging data – By adding (semi)automated image analysis • Things to consider
  4. 4. Innoviris portal introductie • Background
  5. 5. 8-1-2016 pag. 5 What is Whole Slide Imaging (WSI)? • The technique of converting glass-mounted microscopy material into a digital representation / image.
  6. 6. 8-1-2016 pag. 6 Free University of Brussels setup
  7. 7. 8-1-2016 pag. 7 Implementing WSI for end-users Enhanced network infrastructure I gave you slides; how do I get to see these? We’ll send you a link that you can open with your webbrowser Cool!
  8. 8. 8-1-2016 pag. 8 Am I doing digital pathology yet? • Whole Slide Imaging – A technique – Get a scanner – Generate LOTS of data – Easy • Digital (histo)pathology – A method, • a way of thinking – Workflow management, integration – USE the data • valorization – Hard
  9. 9. 8-1-2016 pag. 9 So you want to do digital pathology? Education Biobanking Second opinion Primary diagnosis Research QA Start small; select one use case; implement it RIGHT; scale and build on your success
  10. 10. Innoviris portal introductie • What is a biobank?
  11. 11. 8-1-2016 pag. 11 Why do we build biobanks? – Show-case for patients and samples. – Facilitates patient selection for studies. – Permanent record of cases allows retrospective validation of studies.
  12. 12. 8-1-2016 pag. 12 Flemish Biobank (CMI – BBMRI. vl) • Minimal clinical data (OECD) • Extended epidemiological dataset • HLA, auto-immune data • Images
  13. 13. 8-1-2016 pag. 13 Source material: slides and clinical data
  14. 14. Innoviris portal introductie • Adding value to your biobank
  15. 15. 8-1-2016 pag. 15 Opportunities for digital pathology • Without digital histopathology: • With digital histopathology: I’m looking for breast tumor tissue Does the sample contain cancer cells Is it in situ or invasive? Is it hormone- responsive? I’m looking for breast tumor tissue Does the sample contain cancer cells Is it in situ or invasive? Is it hormone- responsive? Sure; glad to help Just trust us Just trust us We don’t have that information Sure; glad to help Have a look at the HE Have a look at the HE and the immunostains (p63 – calponine) Have a look at the immunostains and the fluo data (FISH) Fidelity increases, confident about requests, investments pay off
  16. 16. 8-1-2016 pag. 16 Valorization through digital pathology As you add more layers of data and move further down the pyramid, the value of the initial sample in the biobank increases and the biobank as a whole is likely to prove more useful.
  17. 17. 8-1-2016 pag. 17 Step 1
  18. 18. 8-1-2016 pag. 18 Step 1: adding imaging data http://histosrv.vub.ac.be/dbb
  19. 19. 8-1-2016 pag. 19 Step 1: adding imaging data http://histosrv.vub.ac.be/dbb
  20. 20. 8-1-2016 pag. 20 Step 1: adding imaging data http://histosrv.vub.ac.be/dbb
  21. 21. 8-1-2016 pag. 21 Biobank “behind the scenes” Custom ASP.net website http://histosrv.vub.ac.be/dbb
  22. 22. 8-1-2016 pag. 22 Pathomation software architecture Custom built Public portal
  23. 23. 8-1-2016 pag. 23 Step 2
  24. 24. 8-1-2016 pag. 24 Step 2: image analysis Step 1: find tissue (Based on work by J. Watson, Pathology Visions meeting 2012)
  25. 25. 8-1-2016 pag. 25 Step 2: image analysis Step 1: find tissue Step 2: locate the islets (Based on work by J. Watson, Pathology Visions meeting 2012)
  26. 26. 8-1-2016 pag. 26 Step 2: image analysis Step 1: find tissue Step 2: locate the islets Step 3: quantitate insulin (Based on work by J. Watson, Pathology Visions meeting 2012)
  27. 27. 8-1-2016 pag. 27 The result: intelligent querying I’m looking for pancreatic tissue from a patient with recent onset diabetes and a susceptible HLA-DQ genotype. At least y% of the islets still have to contain insulin-producing cells Here you go
  28. 28. Innoviris portal introductie • Thank you for taking the time to review these materials • Contact me at yves.sucaet@usa.net to continue the conversation
  29. 29. 8-1-2016 pag. 29 Acknowledgements • Diabetes Research Center, VUB – Yves Sucaet – Silke Smeets – Stijn Piessens – Peter In’t Veld • Dept of Pathology, UZ Brussel – Wim Waelput – Ramses Forsyth CMI

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