Deep Learning for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Motivation: Radiologists face challenges in the accurate prediction of prostate cancer (PCa) with gray-zone PSA levels. Deep learning (DL) evaluated PCa with gray-zone PSA levels remains unclear. Goal(s): The aim of this work was to investigate the comparative performance of DL and radiologists. We trained a 3D DenseNet 121 model for automatic PCa classification with gray-zone PSA levels. Approach: We trained a 3D DenseNet 121 model for automatic PCa classification with gray-zone PSA levels. Results: The DL model yielded an AUC of 0.95 (0.85-1.0) for the identification of PCa with gray-zone PSA levels in the test set, significantly improving performance over the inexperienced radiologists. Impact: The deep learning model yielded an AUC of 0.95 (0.85-1.0) for the identification of PCa with gray-zone PSA levels in the test set, significantly improving performance over the inexperienced radiologists.