Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks

Abstract

Currently, non-invasive imaging techniques such as magnetic resonance imaging (MRI) are emerging as powerful diagnostic tools for prostate cancer (PCa) characterization. This paper focuses on automated PCa classification on VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) diffusion weighted (DW)-MRI, which is a non-invasive microstructural imaging technique that comprises a rich imaging protocol and a tissue computational model to map in vivo histological indices. The contribution of the paper is two fold. Firstly, we investigate the potential of automated, model-free PCa classification on raw VERDICT DW-MRI. Secondly, we attempt to adapt and evaluate novel fully convolutional neural networks (FCNNs) for PCa characterization. We present two neural network architectures that adapt U-Net and ResNet-18 to the PCa classification problem. We train the networks end-to-end on DW-MRI data and evaluate the diagnostic performance employing a 10-fold cross validation approach using data acquired from 103 patients. ResNet-18 outperforms U-Net with an average AUC of $86.7\%$. Our results show promise for the utilization of raw VERDICT DW-MRI data and FCNNs for automating the PCa diagnostic pathway.