title: Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients.
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.
authors.affiliation: Institute for Computational Science, ETH Zürich, Switzerland. thomas.fuchs@inf.ethz.ch
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Fuchs
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Wild
authors.forename: Holger
authors.initials: H
authors.lastname: Moch
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D002292:Carcinoma, Renal Cell / Q000378:metabolism* / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007680:Kidney Neoplasms / Q000401:mortality* / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate; D014407:Tumor Cells, Cultured