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What difference does a difference make?

What difference does a difference make?. Elizabeth Little, Ph.D. 26-Oct- 2010. Talk overview. Introduction Tissue thickness variation Using best histological practices Stain intensity variation due to tissue thickness The difference matters Could impact algorithm functionality.

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What difference does a difference make?

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  1. What difference does a difference make? Elizabeth Little, Ph.D. 26-Oct- 2010

  2. Talk overview • Introduction • Tissue thickness variation • Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters • Could impact algorithm functionality

  3. Systems integration source: www.vagabondish.com

  4. The Hematoxylin & Eosin (H&E) slide • Numbers • In 2009, 330 million histology slides were produced in the United States • 83% (274 million) were stained with H&E • Pathologist • Potential first look at the disease state • Cost • Dollars vs. thousands of dollars for more advanced testing

  5. Impacts of H&E stain variability • Pathologist workflow is impacted by staining variability • Repeat slides • Imaging workflow is also impacted by staining variability • Algorithms can by impacted by stain variability

  6. Antecedents that are helpful for H&E slide image analysis • Control of the stain variation • Under best practices we can control stain variability to a certain degree • Algorithms that are robust against stain variation

  7. Staining variables we cannot control- tissue type affects stain intensity

  8. Pixel count (N) Intensity Level

  9. Staining variables that we have some control over - tissue thickness impacts stain intensity 2 micron 4 micron

  10. Pixel count (N)

  11. Talk overview • Introduction • Tissue thickness variation • Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters • Could impact algorithm functionality

  12. Possible sources of variations in section thickness in the histology laboratory • Fixative • Duration of fixation • Tissue processing • Paraffin • Tissue block • Microtome • Histologist

  13. Objective – measure the sectioning process impact on tissue thickness • 1 tissue block used • 1 microtome • 2 settings • Automated (32 slides per histologist) • Manual (32 slides per histologist) • 2 histologists • 22 years of experience vs. 4 years of experience

  14. Tissue thickness variability testing outline • Section • Tissue was sectioned using a microtome setting of 4 microns • Measure Section Thickness • Interferometry • Stain • H&E • Measure intensity • Whole slide imaging

  15. Measuring tissue thickness using vertical scanning interferometry source: cnx.org

  16. Tissue thickness using interferometric measurements • Glass vs. paraffin • Tissue was not measured • Interferometer limitation • Glass level variability • Measurements taken at 6 locations repeatedly

  17. How well are we using the interferometer?

  18. How good is our tissue thickness measuring system? - gage R & R Equipment variation – 0.79% Operator variation – 0.01% Sample variation – 99.20%

  19. Slice thickness variation – by histologist • Nominal setting was 4 microns • Both Histologists cut significantly thicker than 4 microns • Both Histologists cut at significantly different thicknesses from each other

  20. Manual vs. automated microtomy impact on tissue thickness • Histologist 1 mean thickness was not impacted by microtome setting • Both histologists had statistically significant more variability using the • automated setting as compared to the manual setting

  21. Block influences tissue thickness • Histologist 1 was the cutter • Automated setting used • Tissue 3 was cut significantly thinner than tissues 1 & 2

  22. Summary of tissue thickness measurement results • Histology (location within block, slice selection, soaking, etc.) • Difference in mean tissue thickness • Microtome setting – automated vs. manual • Both histologists were impacted by setting • Block • Blocks 1 and 2 were cut more thickly than block 3

  23. Talk overview • Introduction • Tissue thickness variation • Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters • Could impact algorithm functionality

  24. Stain intensity variation due to tissue thickness - normal breast lymph node study 3 micron 4 micron

  25. Objective – measure tissue thickness impacton stain intensity • Tissue was sectioned and measured for thickness • All slides were stained using the same method • All slides were scanned using whole slide imaging and their average intensities were measured

  26. Lymph node – 1 micron makes a measurable difference

  27. Talk overview • Introduction • Tissue thickness variation • Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters • Could impact algorithm functionality

  28. Grey scale intensity differences Pixel count (N)

  29. Summary • Expected vs. measured is different • The difference is quantifiable • Tissue thickness • Stain intensity • The difference matters • Could impact algorithm functionality • Tissue thickness and stain intensity correlate as expected

  30. Further studies • Intensity vs. tissue type • Microtome bounce • Histology vs. • Drift • Knife • Location in block • Degrees of fixation

  31. Acknowledgments Cindy Connolly Wendy Lange Allison Cicchini Heather Free Aaron Ewoniuk Jonathan Hall Mike Cohen, Ph.D. David Clark, Ph.D.

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