Development and Internal Validation of a Prediction Model For Biochemical Recurrence Following Radical Prostatectomy

This study aimed to develop and validate a predictive model for biochemical recurrence (BCR) after radical prostatectomy in patients with prostate cancer, incorporating clinical, pathological, inflammatory, and 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) imaging parameters. This retrospective study included 240 patients with histopathologically confirmed prostate adenocarcinoma who underwent radical prostatectomy between June 2022 and July 2025. BCR was defined as a postoperative serum prostate-specific antigen (≥0.2 ng/mL). Preoperative clinical variables, systemic immune-inflammation index (SII), PET/CT lesion status, and maximum standardized uptake value (SUVmax) were collected along with postoperative pathological staging. Patients were divided into BCR (n = 64) and non-BCR (n = 176) groups. Univariate and multivariate logistic regression analyses identified independent predictors of BCR. A multivariable predictive model was developed and internally validated using a 70/30 training-validation split. Model performance was evaluated using receiver operating characteristic curves, area under the curve (AUC), calibration, and decision curve analysis. Patients with BCR showed more advanced pathological stage (pT3-4: 96.9% vs. 52.3%, p < 0.001), higher lymph node metastasis rates (59.4% vs. 29.5%, p < 0.001), higher SII (682.45 ± 118.23 vs. 637.08 ± 92.40, p = 0.002), and higher SUVmax values (6.59 ± 1.34 vs. 4.92 ± 1.49, p < 0.001). Multivariate analysis identified pathological stage (OR = 36.814, p < 0.001), lymph node status (OR = 7.286, p < 0.001), SUVmax (OR = 2.732, p < 0.001), and PET-positive lesions (OR = 27.929, p < 0.001) as independent predictors of BCR. The combined predictive model achieved excellent discrimination in training (AUC = 0.952) and validation cohorts (AUC = 0.927). Calibration curves showed agreement between predicted and observed outcomes, and decision curve analysis demonstrated superior net clinical benefit compared with treat-all and treat-none strategies. The integrated model demonstrates excellent discrimination and clinical utility, supporting individualized postoperative risk stratification.