Medicine and Health

Predicting biochemical recurrence of prostate cancer with artificial intelligence

H. Pinckaers, J. V. Ipenburg, et al.

This groundbreaking study explores deep learning's ability to accurately predict biochemical recurrence of prostate cancer post-surgery. With promising results from 685 patients, these innovative findings by Hans Pinckaers and colleagues suggest that machine learning can uncover tissue patterns that might surpass traditional grading systems.

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Abstract

This study investigates the use of deep learning to predict biochemical recurrence of prostate cancer after radical prostatectomy. A deep learning system was trained on H&E-stained microarray cores to predict recurrence, utilizing a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. The biomarker shows strong correlation with biochemical recurrence in both cohorts, and concept-based explanations reveal tissue patterns interpretable by pathologists, suggesting predictive power beyond the ISUP grading system.

Publisher

Communications Medicine

Published On

Jun 08, 2022

Authors

Hans Pinckaers, Jolique van Ipenburg, Jonathan Melamed, Angelo De Marzo, Elizabeth A. Platz, Bram van Ginneken, Jeroen van der Laak, Geert Litjens

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