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Proceedings Paper

Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images
Author(s): Richard Scott; Faisal M. Khan; Jack Zeineh; Michael Donovan; Gerardo Fernandez
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Paper Abstract

The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

Paper Details

Date Published: 23 March 2016
PDF: 7 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910V (23 March 2016); doi: 10.1117/12.2217277
Show Author Affiliations
Richard Scott, Icahn School of Medicine at Mount Sinai (United States)
Faisal M. Khan, Icahn School of Medicine at Mount Sinai (United States)
Jack Zeineh, Icahn School of Medicine at Mount Sinai (United States)
Michael Donovan, Icahn School of Medicine at Mount Sinai (United States)
Gerardo Fernandez, Icahn School of Medicine at Mount Sinai (United States)

Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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