Identification of Prognostic Factors using Quantitative Image Analysis of HER2 Expression by Immunohistochemistry (IHC) in Adenocarcinoma of the Esophagogastric Junction

Günter Schmidt, Gerd Binnig

Definiens AG München

Annette Feuchtinger, Axel Walch

Pathology, HelmholtzZentrum München

52nd Symposium of the Society for Histochemistry

Prague, 1 - 4 September 2010

Study Overview

Surgical Resection Prognostic factor performance Klinikum Rechts der Isar, TU Munich Definiens AG; Biomathematics and

Biometry, Helmholtz Zentrum

Visual HER2 scoring by pathologist Pathology, Helmholtz Zentrum

Illustration

Image: University of California, 1919

Tissue IHC staining and

image acquisition Pathology, Helmholtz Zentrum

Definiens Developer XD, 2010

Quantitative image analysis Definiens AG

Slide - 2

Slide 3

Data: Tissue Micro Arrays of Biopsy Tissue Sections

132 cancer patients �

390 tissue cores on 3 TMAs �

HER2 (human epidermal �growth factor receptor 2)

� Membrane protein

� Known to indicate

aggressive cancer subtypes

-

Pathologist Score 3+ Score depends an membrane staining intensity, staining completeness,

and percentage of stained tumor cells

5x

20x

Slide - 4

Slide - 5

Pathologist Score 2+

Slide - 6

Pathologist Score 1+

Slide - 7

Pathologist Score 0

Pathologist Score As Prognostic Factor Score 0, 1+, 2+ versus 3+

Disease Free Survival Overall Survival

Slide - 8

Slide 9

-

Automated Image Analysis with Definiens Platform Step 1. TMA core detection and grid assignment

Slide 10

-

Automated Image Analysis with Definiens Platform Step 2. Cell and cell compartment segmentation and classification

Slide 11

-

Multi-hierarchical Segmentation: Cells

Slide 12

-

Multi-hierarchical Segmentation: Nucleus, Cytoplasm and Membrane

Slide 13

-

Multi-hierarchical Segmentation: Nucleus and Membrane Substructure

Sample Image Analysis Results I

Slide - 14

Sample Image Analysis Results II

Slide - 15

Sample Image Analysis Results III

Slide - 16

Slide 17

-

Quantitative Image Analysis Results

(54) image features Regression Learner Goals

Slide 18

-

Multivariate Regression Analysis to Predict Survival Time

Use Predicted Disease Free Survival Time as Prognostic Factor Kaplan Meier analysis of disease free survival time

Slide - 19

Use Predicted Overall Survival Time as Prognostic Factor Kaplan Meier analysis of overall survival time

Slide - 20

Disease Free Survival Time Prediction after Feature Space Reduction Kaplan Meier analysis indicates significant prognostic value (2 fold cross validated)

Single object properties �

� cell_brown(q05)*

� cell_brown(q50)

� cell_brown(q95)

Properties of object relations �

� membrane_cytoplasm_ratio_red(q05)

� membrane_cytoplasm_ratio_red(q50)

� membrane_cytoplasm_ratio_red(q95)

� membrane_cytoplasm_ratio_green(q05)

� membrane_cytoplasm_ratio_green(q50)

� membrane_cytoplasm_ratio_green(q95)

(*) q05/50/95 are 5%/50%/95% quantiles of object feature values per core

Slide - 21

Summary

Automated quantitative image analysis �

� Extracts rich set of image object measurements previously not accessible to

biologist / pathologist

� Provides statistically significant prognostics factors

Definiens Cognition Network Technology comprises �

� Context driven segmentation and classification generates multi-hierarchical

network of image objects

� Comprehensible image analysis process

Definiens image analysis platform is �

� Open for integration: image acquisition, algorithms, data bases

� Scalable using distributed, load balanced, computer grid

� See more at www.definiens.com

Slide - 22