Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death among men. To improve patient outcomes, treatment must be personalized based on accurate prognosis. Nomograms already exist to identify patients at low risk for recurrence based on preoperative clinical information, but these tools do not use patients’ medical images.

The goal of this project is to use deep learning to develop a model combining FDG-PET/CT images and patient clinical data to improve the pre-treatment prognosis of high-grade prostate cancer. This model must be efficient, but also interpretable in order to allow an expert to understand the given probabilities.