Prostate cancer progresses more slowly than other cancers and can be cured with appropriate treatment. Recently, the use of minimally invasive robot-assisted radical prostatectomy (RARP) in surgery, one of the treatment methods, has made it possible to return to society earlier than with conventional methods. However, the impact of urinary incontinence, which is a side effect, on patient’s daily lives cannot be ignored, and prediction of postoperative urinary continence is expected to assist in the selection of treatment options. In this study, magnetic resonance images taken at the time of diagnosis at multiple facilities were preprocessed, and then classified into good and bad cases of urinary continence using 3D convolutional neural network (3DCNN) and machine learning methods. The degree of urinary continence was defined as good or bad based on the number of urinary pads changed per day at 3 months postoperatively. As a result, accuracy for good urinary continence was 83.7%, accuracy for bad urinary continence was 67.1%, and Balanced accuracy was 75.4%. These results indicate that the classification accuracy is higher than that of previous studies on 2D images, suggesting that this method may be an effective technique for predicting postoperative urinary continence in prostate cancer patients.
