Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients.
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.
Fuchs
Thomas J
TJ
Institute for Computational Science, ETH Zürich, Switzerland. thomas.fuchs@inf.ethz.ch
Wild
Peter J
PJ
Moch
Holger
H
Buhmann
Joachim M
JM
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