Development and Validation of a Non-Invasive AI Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT

Prostate cancer is one of the most common malignancies in men. Currently, due to the limited diagnostic accuracy of existing imaging tests, there is a risk of missed diagnosis or unnecessary prostate biopsy. This study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model using two advanced imaging techniques: multiparametric MRI (mpMRI) and PSMA PET/CT. By integrating information from both imaging modalities, the AI model is expected to improve the diagnostic accuracy of prostate cancer, reduce unnecessary biopsies, and assist physicians in making better clinical decisions. This is a retrospective, multicenter study that plans to collect imaging and pathology data from approximately 1,000 to 1,500 patients across six major hospitals in China. The diagnostic performance of the model will be evaluated, including its ability to identify clinically significant prostate cancer and its value in assisting diagnosis in patients with PSA levels in the gray zone (4-20 ng/mL).

Inclusion Criteria: 1. Age ≥ 18 years. 2. ECOG performance status 0-2. 3. Life expectancy \> 6 months. 4. Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export. 5. Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard. 6. Complete clinical data available, including pre-treatment PSA (tPSA, fPSA), TNM stage, Gleason score, PI-RADS score, SUVmax, prostate volume (from MRI/PSMA PET), age, and BMI. 7. Informed consent for data use for research purposes according to each center's ethics requirements. Exclusion Criteria: 1. History of other malignant tumors 2. Previous prostate surgery (e.g., TURP) 3. Prior endocrine therapy or radiotherapy 4. Severe renal insufficiency 5. Major organ dysfunction or life expectancy \< 1 year