Detection of Local Prostate Cancer Recurrence from PET/CT Scans Using Deep Learning

Prostate cancer (PC) is a leading cause of cancer-related deaths in men worldwide. PSMA-directed positron emission tomography (PET) has shown promising results in detecting recurrent PC and metastasis. We developed a deep-learning model to predict cancer recurrence from PSMA PET/CT images of prostate cancer patients after treatment. Different methods were used to improve the performance of the initial model, such as modifying the region of interest (ROI), including metadata as additional layers, or passing prostatectomy state to the model. A hyperparameter optimization of multiple parameters was performed to further increase the model's performance, which, combined with including metadata as additional image layers, resulted in the best validation accuracy of 77 %. Even though the final validation accuracy fell short of reaching 90 %, significant improvements were made to the model, and many approaches were tested that can be a basis for further research and improvements toward the performance of a model that can confidently detect cancer remission in the prostate or prostate bed.