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

  • Thomas J. Fuchs
  • Peter J. Wild
  • Holger Moch
  • Joachim M. Buhmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


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.


Renal Cell Carcinoma Random Forest Local Binary Pattern Face Detection Expert Pathologist 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-540-85990-1_1_MOESM1_ESM.pdf (177 kb)
Electronic Supplementary Material (177 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas J. Fuchs
    • 1
    • 3
  • Peter J. Wild
    • 2
  • Holger Moch
    • 2
    • 3
  • Joachim M. Buhmann
    • 1
    • 3
  1. 1.Institute for Computational ScienceETH ZürichSwitzerland
  2. 2.Institute of PathologyUniversity Hospital Zürich, University ZürichSwitzerland
  3. 3.Competence Center for Systems Physiology and Metabolic DiseasesETH ZürichSwitzerland

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