# Automated Gleason Grading of Prostate Biopsies using Deep Learning

## TL;DR

This paper presents a deep learning system that automates Gleason grading of prostate biopsies, reducing variability and outperforming many pathologists, thereby potentially improving prostate cancer prognosis.

## Contribution

The study introduces a fully automated deep learning approach for prostate biopsy grading, utilizing semi-automatic labeling to reduce manual annotation efforts.

## Key findings

- High agreement with reference standard
- Outperformed 10 of 15 pathologists in observer experiment
- Potential to improve prostate cancer prognostics

## Abstract

The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists. The system has the potential to improve prostate cancer prognostics by acting as a first or second reader.