Chengyan Wang1, Juan Hu2, He Wang2, Hui Zhang1, Rui Wang2, Wenchao Cai2, Wei Wang2, Xiaoying Wang, 12, Jue Zhang3, 4, Jing Fang, 13
1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; 2Department of Radiology, Peking University First Hospital, Beijing, China; 3College of Engineering, Peking University, Beijing, China; 4Academy for Advanced Interdisciplinary Studies, Peking University , Beijing, China
In the present study, we propose a multi-layer artificial neural network (ANN) model integrating MR images (T2 weighted imagesdiffusion weighted images, dynamic contrast enhanced images and MR spectroscopy) and clinical examination (Total PSA value, free/total PSA ratio and age) for prostate cancer detection. The new model is able to provide high accuracy in detection of prostate cancer through proper training. Significant improvement in the AUC can be produced when compared to clinical-only model, and this demonstrates the capacity of MRI for computer-aided prostate cancer identification.