Micro-ultrasound is a promising new technique offering high-resolution images to identify prostate cancer. However, clinical interpretation of the images remains challenging. The underlying raw radio-frequency (RF) ultrasound data acquired during imaging is a unique source of tissue acoustic properties, potentially ideal to identify suspicious areas to target at biopsy. Here, we develop a convolutional neural network (3D CNN) to classify tissue as benign vs clinically significant prostate cancer (csPCa) using the raw RF data.