Preoperative prediction of pathological pelvic lymph node metastasis in prostate cancer using an elastic net model integrating clinicopathologic and ⁶⁸Ga-PSMA PET/CT features: development and temporal validation

Preoperative decision-making regarding extended pelvic lymph node dissection (ePLND) in prostate cancer remains challenging. Existing nodal-risk prediction tools are mainly based on clinicopathologic variables and may show suboptimal performance across different clinical settings. This study aimed to develop and temporally validate a preoperative prediction model for pathological pelvic lymph node metastasis (pN1) by integrating clinicopathologic and ⁶⁸Ga-PSMA PET/CT-derived features in a single-center Chinese surgical cohort. In this single-center retrospective study, consecutive patients with prostate cancer who underwent robot-assisted radical prostatectomy with ePLND and preoperative ⁶⁸Ga-PSMA PET/CT were screened. Of 617 patients assessed for eligibility, 315 met the final analytic criteria. Patients treated from 2020 to 2024 formed the development cohort (n = 210), and patients treated in 2025 served as an independent temporal validation cohort (n = 105). Two elastic net regularized logistic regression models were developed using preoperative variables: a clinicopathologic model and an integrated model combining clinicopathologic and PSMA PET/CT-derived features. Internal robustness was assessed using 50 repeated stratified random splits of the development cohort. Final locked models were then applied, without re-tuning, to the temporal validation cohort. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis. The Memorial Sloan Kettering Cancer Center (MSKCC) model was evaluated as a benchmark. In the development cohort, 57/210 (27.1%) patients had pathologically confirmed pN1 disease, compared with 27/105 (25.7%) in the temporal validation cohort. Across 50 repeated stratified splits in the development cohort, the clinicopathologic model achieved a mean test AUC of 0.910 (SD 0.029), whereas the integrated model achieved a mean test AUC of 0.962 (SD 0.022). In the temporal validation cohort, the clinicopathologic model achieved an AUC of 0.877 (95% CI 0.799–0.955), whereas the integrated model achieved an AUC of 0.943 (95% CI 0.898–0.989). The integrated model showed sensitivity of 0.852, specificity of 0.897, negative predictive value of 0.946, accuracy of 0.886, and a Brier score of 0.080. Calibration was acceptable, with more favorable calibration for the integrated model than for the clinicopathologic model. Decision curve analysis showed greater net benefit for the integrated model than for both the clinicopathologic model and the MSKCC benchmark across most clinically relevant threshold probabilities. In this single-center Chinese surgical cohort, an elastic net model integrating clinicopathologic and ⁶⁸Ga-PSMA PET/CT-derived features showed favorable performance for preoperative pN1 prediction, including in temporal validation. Compared with a clinicopathologic model alone and a locally implemented MSKCC benchmark, the integrated model showed more favorable discrimination and potential clinical utility. Multicenter external validation is required before broader clinical application.