Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images

Faisal M. Khan; Richard Scott; Michael Donovan; Gerardo Fernandez

doi:10.1117/1.JMI.4.2.021103

28 February 2017 Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images

Faisal M. Khan, Richard Scott, Michael Donovan, Gerardo Fernandez

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 2, 021103 (February 2017). https://doi.org/10.1117/1.JMI.4.2.021103

Abstract

The Gleason grade is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous algorithms developed to approximate and duplicate the Gleason scoring system, mostly developed in standard H&E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be robust in developing prognostic assessments of disease, particularly in prostate cancer. We leverage a method of segmenting gland rings in IF images for predicting the pathological Gleason, both the clinical and the image specific grades, which may not necessarily be the same. We combine these measures with nuclear specific characteristics. In 324 images from 324 patients, 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 [concordance index (CI) of 0.89], significantly outperforming the clinical Gleason grades (CI of 0.78). Finally, in multivariate models for multiple prostate cancer progression endpoints, replacing the Gleason with these features results in equivalent or improved performances. This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade, and even replacing it in prostate cancer prognostics.

Citation Download Citation

Faisal M. Khan, Richard Scott, Michael Donovan, and Gerardo Fernandez "Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images," Journal of Medical Imaging 4(2), 021103 (28 February 2017). https://doi.org/10.1117/1.JMI.4.2.021103

Received: 31 July 2016; Accepted: 27 December 2016; Published: 28 February 2017

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