Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI

Abstract

Prostate Cancer (PCa) is highly prevalent and is the second most common cause of cancer-related deaths in men. Multiparametric MRI (mpMRI) is robust in detecting PCa. We developed a weakly supervised computer-aided detection (CAD) system that uses biopsy points to learn to identify PCa on mpMRI. Our CAD system, which is based on a deep convolutional neural network architecture, yielded an area under the curve (AUC) of 0.903±0.009 on a receiver operation characteristic (ROC) curve computed on 10 different models in a 10 fold cross-validation. 9 of the 10 ROCs were statistically significantly different from a competing support vector machine based CAD, which yielded a 0.86 AUC when tested on the same dataset (α = 0.05). Furthermore, our CAD system proved to be more robust in detecting high-grade transition zone lesions.