Development and Validation of an AI ‐Assisted Predictive Model Integrating R2 * Mapping and Clinical Indicators for Clinically Significant Prostate Cancer

ABSTRACT Background Limited evidence exists on the diagnostic performance of Artificial Intelligence (AI)‐assisted Simplified Prostate Imaging Reporting and Data System version 2.1 (S‐PI‐RADS v2.1) combined with quantitative MRI parameters for detecting clinically significant prostate cancer (csPCa). Purpose To develop and validate a nomogram incorporating AI‐assisted S‐PI‐RADS v2.1 (based on biparametric MRI bpMRI) and R2* mapping for csPCa prediction. Methods This prospective study enrolled 345 patients grouped by pathology: non‐csPCa with benign prostatic hyperplasia ( n = 230) and csPCa ( n = 115). Clinical (age, body mass index BMI, prostate‐specific antigen PSA, free PSA) and imaging parameters (prostate volume PV, S‐PI‐RADS score, R2*) were analyzed. Independent predictors were identified via logistic regression. A nomogram was developed using R software with the DynNom package (Version 2.0) and validated (1000 bootstrap iterations), with performance assessed by area under the curve (AUC), calibration, decision curve analysis (DCA), and DeLong test ( p < 0.05 significant). Results Independent csPCa predictors included BMI, PSA ≥ 10 ng/mL, PV, S‐PI‐RADS scores 4–5, and R2* (all p < 0.05). The full model (BMI + PSA + PV + S‐PI‐RADS + R2*) showed superior discrimination (AUC = 0.915) versus the baseline model (AUC = 0.891, p = 0.008), with 85.2% sensitivity and 80.9% specificity. Internal validation was robust (C‐index = 0.884). DCA confirmed clinical utility. An interactive nomogram was deployed ( https://aiguangyong2025.shinyapps.io/dynnomapp/ ). Conclusion The AI‐enhanced nomogram integrating clinical and multiparametric MRI data accurately predicts csPCa noninvasively, with R2* significantly improving performance. This tool facilitates personalized clinical decision‐making.