The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis

Prostate-specific antigen (PSA) testing has limited specificity for prostate cancer diagnosis, leading to a high rate of unnecessary biopsies. This multi-center study aims to develop and validate a non-invasive, multi-modal artificial intelligence model that combines cell-free DNA (cfDNA) profiles with multi-parametric MRI (mpMRI). The primary goal is to improve the accuracy of prostate cancer detection and risk stratification, particularly for men with PSA levels in the 4-10 ng/mL "gray zone," thereby providing a robust tool to guide clinical decision-making and reduce avoidable invasive procedures.

Inclusion Criteria: * Men aged 18-80 years with a clinical indication for prostate or pelvic magnetic resonance (MR) examination. * Patients with normal prostate, benign prostatic hyperplasia, or prostate cancer. * First visit on January 1, 2014, or later. Exclusion Criteria: * Diagnosis of any other malignancy within the previous 5 years. * Prior transurethral resection or enucleation of the prostate before imaging. * Any condition deemed by the investigator to make the patient unsuitable for study participation.