28 February 2017 Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Faisal M. Khan, Faisal M. Khan, Richard Scott, Richard Scott, Michael Donovan, Michael Donovan, Gerardo Fernandez, 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 . Submission: Received: 31 July 2016; Accepted: 27 December 2016
Received: 31 July 2016; Accepted: 27 December 2016; Published: 28 February 2017
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