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 Systems integration source: www.vagabondish.com 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 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 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 Staining variables we cannot control - tissue type affects stain intensity Grey scale intensity differences - skin vs. kidney 10000 Pixel count (N) 8000 6000 Kidney Skin 4000 2000 0 250 200 150 100 Intensity Level 50 0 Staining variables that we have some control over - tissue thickness impacts stain intensity 2 micron 4 micron Grey scale intensity difference due to tissue thickness 5000 4000 Pixel count (N) 2 micron slice 3000 4 micron slice 2000 1000 0 255 204 153 102 Intensity level 51 0 Talk overview • Introduction • Tissue thickness variation – Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters – Could impact algorithm functionality Possible sources of variations in section thickness in the histology laboratory • Fixative • Duration of fixation • Tissue processing • Paraffin • Tissue block • Microtome • Histologist 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 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 Measuring tissue thickness using vertical scanning interferometry source: cnx.org Tissue thickness using interferometric measurements • Glass vs. paraffin • Tissue was not measured •Interferometer limitation •Glass level variability • Measurements taken at 6 locations repeatedly How well are we using the interferometer? Source Standa % deviati Total (gage) 0.29 0.80% Repeatability equipment 0.29 0.79% Reproducibilit 0.03 operator 0.01% Slide variation 3.20 99.20% Total 100.00% 3.21 How good is our tissue thickness measuring system? - gage R & R Equipment variation – 0.79% Operator variation – 0.01% Equipment Variation Operator Variation Sample variation – 99.20% Tissue Thickness Variation Slice thickness variation – by histologist Histologis Number slides Combined 128 1 2 64 64 Measured average S.D. (mm) 4.74 ± 0.16 4.65 ± 0.10 4.84 ± 0.16 • Nominal setting was 4 microns • Both Histologists cut significantly thicker than 4 microns • Both Histologists cut at significantly different thicknesses from each other Manual vs. automated microtomy impact on tissue thickness Histologist Microtome setting Measured thickness ± S.D. (mm) 1 Automated 4.65 ± 0.13 Manual 4.65 ± 0.08 Automated 4.91 ± 0.16 Manual 4.76 ± 0.12 2 • 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 Block influences tissue thickness Tissue Measured block average ± (um) Tissue 4.65 ± 0.13 (n=32) Tissue 4.60 ± 0.12 (n=16) Tissue 4.36 ± 0.12 three (n=16) • Histologist 1 was the cutter • Automated setting used • Tissue 3 was cut significantly thinner than tissues 1 & 2 Summary of tissue thickness measurement results 1. Histology (location within block, slice selection, soaking, etc.) • Difference in mean tissue thickness 2. Microtome setting – automated vs. manual • Both histologists were impacted by setting 3. Block • Blocks 1 and 2 were cut more thickly than block 3 Talk overview • Introduction • Tissue thickness variation – Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters – Could impact algorithm functionality Stain intensity variation due to tissue thickness - normal breast lymph node study 3 micron 4 micron Objective – measure tissue thickness impact on 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 Lymph node – 1 micron makes a measurable difference Effects of tissue thickness on intensity 250 Intensity 200 150 Intensity 100 Linear Fit 50 0 2 2.5 3 3.5 4 Tissue thickness ( mm) 4.5 5 Talk overview • Introduction • Tissue thickness variation – Using best histological practices • Stain intensity variation due to tissue thickness • The difference matters – Could impact algorithm functionality Grey scale intensity differences Effects of tissue thickness on binning 5000 Pixel count 4000 2.62 micron 3.32 micron 3.43 micron 4.37 micron (N) 3000 2000 1000 0 255 204 153 102 Intensity level 51 0 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 Further studies • Intensity vs. tissue type • Microtome bounce • Histology vs. – – – – Drift Knife Location in block Degrees of fixation Acknowledgments Cindy Connolly Wendy Lange Allison Cicchini Heather Free Aaron Ewoniuk Jonathan Hall Mike Cohen, Ph.D. David Clark, Ph.D.