Electrical Engineering and Systems Science

Bulten, Wouter;Pinckaers, Hans;van Boven, Hester;Vink, Robert;de Bel, Thomas;van Ginneken, Bram;van der Laak, Jeroen;de Kaa, Christina Hulsbergen-van;Litjens, Geert

2019 Electrical Engineering and Systems Science

doi: 10.1016/S1470-2045(19)30739-9pmid: 31926805

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.