Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients

Authors:
Thomas J. Fuchs;Peter J. Wild;Holger Moch;Joachim M. Buhmann
Affiliations:
Institute for Computational Science, ETH Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,;Institute of Pathology, University Hospital Zürich, University Zürich, Switzerland;Institute of Pathology, University Hospital Zürich, University Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,;Institute for Computational Science, ETH Zürich, Switzerland and Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich,
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

Renal cell carcinoma (RCC) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different RCC patients. The application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists.