Definiens Digital Pathology Image Analysis More Accurate than Manual Evaluation of Her2 for Esophageal Cancer Prognostication
Posted on Friday, September 17, 2010
From the editor: While it has long been suspected that digital pathology image analysis may have great promise in assisting in the accuracy of diagnosis / prognosis of disease, very few studies have actually proven this.
Dr. Guenter Schmidt et al. have recently presented their findings in a study to compare manual H-score evaluation (Dako Herceptest) of esophagogastric TMA cores as compared to an algorithm derived at Definiens, using Definiens Developer™ software.
The findings are quite stunning: Kaplan-Meier Analysis revealed a significant (DFS: p < 2.4×10-6; OS: p < 2.5×10-7) prognostic value for the two groups generated by data mining image analysis results, whereas the visually assessed score was not significant (p>0.1).
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 < 2.4×10-6; OS: p < 2.5×10-7) prognostic value for the two groups generated by data mining image analysis results, whereas the visually assessed score was not significant (p>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)