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. © 2008 Springer Berlin Heidelberg.
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CITATION STYLE
Fuchs, T. J., Wild, P. J., Moch, H., & Buhmann, J. M. (2008). Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5242 LNCS, pp. 1–8). Springer Verlag. https://doi.org/10.1007/978-3-540-85990-1_1
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