Clinically Significant Prostate Cancer

This is a multicenter observational study. A deep learning model integrated with multimodal imaging and digital pathology spatial registration is built based on preoperative multiparametric magnetic resonance imaging, transrectal ultrasound and postoperative digital pathological whole slide images. The study is designed to achieve accurate prediction of clinically significant prostate cancer and non-invasive risk stratification. Unnecessary prostate biopsy and overdiagnosis can be reduced to support the optimization of clinical diagnosis and treatment strategies.

The aim of the present study is to investigate a plasma exosome RNA signature to diagnose clinically significant prostate cancer.

This is a multicenter, prospective diagnostic accuracy study evaluating the Istanbul PSMA PET/CT Criteria (IPPC) for selecting patients with biopsy-confirmed low-risk prostate cancer (ISUP Grade 1) for active surveillance (AS). The study integrates delayed Ga-68 PSMA PET/CT imaging into the diagnostic pathway to refine patient stratification, minimize overtreatment and potentially reduce unnecessary biopsies and MRI, or exposing high-risk individuals to the danger of cancer progression if left untreated scans.