What difference does a difference make Elizabeth Little

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

Pixel count (N) Intensity Level

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

Pixel count (N)

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 Standard deviation % Contribution Total measurement (gage) 0. 29 0. 80% Repeatability – equipment variation 0. 29 0. 79% Reproducibility – operator variation 0. 03 0. 01% Slide variation 3. 20 99. 20% Total variation 3. 21 100. 00%

How good is our tissue thickness measuring system? - gage R & R Equipment variation – 0. 79% Sample variation – 99. 20% Operator variation – 0. 01%

Slice thickness variation – by histologist Histologist Number of slides Measured thickness average ± S. D. (mm) Combined 128 4. 74 ± 0. 16 1 64 4. 65 ± 0. 10 2 64 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 block Measured thickness average ± S. D. (um) Tissue one 4. 65 ± 0. 13 Tissue two 4. 60 ± 0. 12 • Automated setting used 4. 36 ± 0. 12 • Tissue 3 was cut significantly thinner than tissues 1 & 2 (n=32) (n=16) Tissue three (n=16) • Histologist 1 was the cutter

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

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 Pixel count (N)

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.