Paper
19 March 2015 Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis
Richard Scott, Faisal M. Khan, Jack Zeineh, Michael Donovan, Gerardo Fernandez
Author Affiliations +
Abstract
Immunofluorescent (IF) image analysis of tissue pathology has 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. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.
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Richard Scott, Faisal M. Khan, Jack Zeineh, Michael Donovan, and Gerardo Fernandez "Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis", Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200E (19 March 2015); https://doi.org/10.1117/12.2082389
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KEYWORDS
Autoregressive models

Proteins

Tissues

Prostate cancer

Feature extraction

Biopsy

Prostate

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