Your SlideShare is downloading. ×
  • Like
1390 Identification Of Prognostic Factors Using Quantitative Image Analysis Of Her2 Expression.Pdf 1390
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

1390 Identification Of Prognostic Factors Using Quantitative Image Analysis Of Her2 Expression.Pdf 1390

  • 638 views
Published

Schmidt G, Binnig G, Feuchtinger A, Walch A : Identification of Prognostic Factors using Quantitative Image Analysis of HER2 Expression by Immunohistochemistry (IHC) in Adenocarcinoma of the …

Schmidt G, Binnig G, Feuchtinger A, Walch A : Identification of Prognostic Factors using Quantitative Image Analysis of HER2 Expression by Immunohistochemistry (IHC) in Adenocarcinoma of the Esophagogastric Junction Background: Since adenocarcinoma of the oesophagogastric junction is known to show human epidermal growth factor receptor 2 (HER2) overexpression we investigated the potential of IHC stained cancer tissue to provide information about disease free (DFS) and overall survival (OS) times. We compare the prognostic value of a visually assessed scoring algorithm derived from Dako HercepTestTM with results provided by data mining information from quantitative image analysis. Methods: Three tissue microarrays (TMAs) comprising 391 cores from tissue samples of 150 patients were analysed. After IHC staining with HER2 antibody the TMAs were scanned with Zeiss MIRAX slide scanner (20x objective). Fully automated image analysis using the Definiens Cognition Network Technology® segmented and classified cells, nuclei, cytoplasm and membrane objects, and determined on a per cell basis shape, texture and color properties. Those were correlated with known DFS/OS times using a multivariate regression analysis within the R statistical software. Based on this predictive model, the patient population was divided in one group with good and one with poor prognosis by imposing a threshold on the predicted survival times. The corresponding groups obtained by the pathologist scoring were HER2 score 0, 1+, 2+ versus HER2 score 3+. Result: Kaplan-Meier Analysis revealed a significant (DFS: p < 2.4x10-6; OS: p < 2.5x10-7) prognostic value for the two groups generated by data mining image analysis results, whereas the visually assessed score was not significant (p&gt;0.1). Conclusion: Data mining quantitative image analysis may provide a more accurate evaluation of HER2 evaluation than a visual assessment of tissue samples. The quantification of HER2 overexpression by image analysis may be also highly valuable for the prediction of anti-HER2 therapy in combating this cancer type. (Presentation given at the 52nd Symposium of the Society for Histochemistry, Prague, Sept 1 - 4)

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
638
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
10
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. 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
  • 2. 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 Slide - 2 Quantitative image analysis Definiens AG
  • 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 Slide - 3
  • 4. Pathologist Score 3+ Score depends an membrane staining intensity, staining completeness, and percentage of stained tumor cells 5x 20x Slide - 4
  • 5. Pathologist Score 2+ Slide - 5
  • 6. Pathologist Score 1+ Slide - 6
  • 7. Pathologist Score 0 Slide - 7
  • 8. Pathologist Score As Prognostic Factor Score 0, 1+, 2+ versus 3+ Disease Free Survival Overall Survival Slide - 8
  • 9. Automated Image Analysis with Definiens Platform Step 1. TMA core detection and grid assignment Slide - 9
  • 10. Automated Image Analysis with Definiens Platform Step 2. Cell and cell compartment segmentation and classification Slide - 10
  • 11. Multi-hierarchical Segmentation: Cells Slide - 11
  • 12. Multi-hierarchical Segmentation: Nucleus, Cytoplasm and Membrane Slide - 12
  • 13. Multi-hierarchical Segmentation: Nucleus and Membrane Substructure Slide - 13
  • 14. Sample Image Analysis Results I Slide - 14
  • 15. Sample Image Analysis Results II Slide - 15
  • 16. Sample Image Analysis Results III Slide - 16
  • 17. Quantitative Image Analysis Results Regression Learner Goals (54) image features Slide - 17
  • 18. Multivariate Regression Analysis to Predict Survival Time Slide - 18
  • 19. Use Predicted Disease Free Survival Time as Prognostic Factor Kaplan Meier analysis of disease free survival time Slide - 19
  • 20. Use Predicted Overall Survival Time as Prognostic Factor Kaplan Meier analysis of overall survival time Slide - 20
  • 21. 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
  • 22. 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