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Proceedings Paper

Glandular object based tumor morphometry in H&E biopsy samples for prostate cancer prognosis
Author(s): Stephen I. Fogarasi; Faisal M. Khan; Ho-Yuen H. Pang; Ricardo Mesa-Tejada; Michael J. Donovan; Gerardo Fernandez
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Paper Abstract

Morphological and architectural characteristics of primary prostate tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide critical information for cancer diagnosis, prognosis and therapeutic response prediction. The subjective and variable Gleason grade assessed by expert pathologists in Hematoxylin and Eosin (H&E) stained specimens has been the standard for prostate cancer diagnosis and prognosis. We propose a novel morphometric, glandular object-oriented image analysis approach for the robust quantification of H&E prostate biopsy images. We demonstrate the utility of features extracted through the proposed method in predicting disease progression post treatment in a multi-institution cohort of 1027 patients. The biopsy based features were univariately predictive for clinical response post therapy; with concordance indexes (CI) ≤ 0.4 or ≥ 0.6. In multivariate analysis, a glandular object feature quantifying tumor epithelial cells not directly associated with an intact tumor gland was selected in a model incorporating preoperative clinical data, protein biomarker and morphological imaging features. The model achieved a CI of 0.73 in validation, which was significantly higher than a CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice. This work presents one of the first demonstrations of glandular object based morphological features in the H&E stained biopsy specimen to predict disease progression post primary treatment. Additionally, it is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

Paper Details

Date Published: 9 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633F (9 March 2011); doi: 10.1117/12.878142
Show Author Affiliations
Stephen I. Fogarasi, Aureon Biosciences, Inc. (United States)
Faisal M. Khan, Aureon Biosciences, Inc. (United States)
Ho-Yuen H. Pang, Aureon Biosciences, Inc. (United States)
Ricardo Mesa-Tejada, Aureon Biosciences, Inc. (United States)
Michael J. Donovan, Aureon Biosciences, Inc. (United States)
Gerardo Fernandez, Aureon Biosciences, Inc. (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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