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Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients


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: Metaxas, D. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. Pt. 2. Berlin: Springer, 1-8.

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.

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.

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Additional indexing

Item Type:Book Section, not refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Pathology and Molecular Pathology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2008
Deposited On:28 Jan 2009 08:24
Last Modified:05 Apr 2016 12:52
Publisher:Springer
Series Name:Lecture Notes in Computer Science
ISBN:978-3-540-85989-5
Publisher DOI:https://doi.org/10.1007/978-3-540-85990-1_1
Related URLs:http://opac.nebis.ch/F/?local_base=NEBIS&con_lng=GER&func=find-b&find_code=SYS&request=005703726
PubMed ID:18982583

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