A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology

• URL: http://arxiv.org/abs/2209.13408v1
• Date: Tue, 27 Sep 2022 14:08:19 GMT
• Title: A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology
• Authors: Alessandro Ferrero, Beatrice Knudsen, Deepika Sirohi, Ross Whitaker
• Abstract summary: Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. The cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. For diagnostic work-up, pathologists first locate glands in the whole biopsy core, and -- if they detect cancer -- they assign a Gleason grade. This time-consuming process is subject to errors and significant inter-observer variability, despite strict diagnostic criteria. This paper proposes an automated workflow that follows pathologists' \textit{modus operandi}, isolating and classifying multi-scale patches of individual glands in whole slide images (WSI) of biopsy tissues using distinct steps: (1) two fully convolutional networks segment epithelium versus stroma and gland boundaries, respectively; (2) a classifier network separates benign from cancer glands at high magnification; and (3) an additional classifier predicts the grade of each cancer gland at low magnification. Altogether, this process provides a gland-specific approach for prostate cancer grading that we compare against other machine-learning-based grading methods.

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