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

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Grey scale intensity differences - skin vs. kidney

Kidney

Skin

Intensity Level

Pixelcount(N)

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

2 micron 4 micron

255 204 153 102 51 00

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2000

3000

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Intensity level

Grey scale intensity difference due to tissue thickness

2 micron slice

4 micron slice

Pixelcount(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%

Equipment Variation

Operator Variation

Tissue ThicknessVariation

How good is our tissue thickness measuring system? - gage R & R

Equipment

variation – 0.79%

Operator

variation – 0.01%

Sample

variation – 99.20%

Slice thickness variation – by histologistHistologi

stNumbe

r of slides

Measured thickness average ± S.D. (m)

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

Microtomesetting

Measuredthickness ± S.D. (m)

1 Automated 4.65 ± 0.13

Manual 4.65 ± 0.08

2 Automated 4.91 ± 0.16

Manual 4.76 ± 0.12

• 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(n=32)

4.65 ± 0.13

Tissue two(n=16)

4.60 ± 0.12

Tissue three(n=16)

4.36 ± 0.12

• 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 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

Lymph node – 1 micron makes a measurable difference

Effects of tissue thickness on intensity

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2 2.5 3 3.5 4 4.5 5

Tissue thickness (m)

Inte

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ty

Intensity

Linear Fit

Talk overview

• Introduction

• Tissue thickness variation– Using best histological practices

• Stain intensity variation due to tissue thickness

• The difference matters– Could impact algorithm functionality

255 204 153 102 51 00

1000

2000

3000

4000

5000

Intensity level

Effects of tissue thickness on binning

2.62 micron

3.32 micron

3.43 micron

4.37 micron

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.