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

TL;DR

This paper introduces an automated, gland-specific workflow for prostate cancer grading in histopathology images, mimicking pathologists' methods to improve accuracy and reduce variability.

Contribution

It presents a novel multi-step deep learning approach that segments, classifies, and grades prostate glands in whole slide images, aligning with pathologists' diagnostic process.

Findings

Achieved accurate segmentation of prostate tissue components.

Successfully distinguished benign from cancerous glands.

Provided gland-specific grading comparable to expert assessments.

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.