External validation of nomograms including PSMA PET information for the prediction of lymph node involvement of prostate cancer

Abstract

Background

Novel nomograms predicting lymph node involvement (LNI) of prostate cancer (PCa) including PSMA PET information have been developed. However, their predictive accuracy in external populations is still unclear.

Purpose

To externally validate four LNI nomograms including PSMA PET parameters (three Muehlematter models and the Amsterdam-Brisbane-Sydney model) as well as the Briganti 2012 and MSKCC nomograms.

Methods

Patients with histologically confirmed PCa undergoing preoperative MRI and PSMA PET/CT before radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) were included. Model discrimination (AUC), calibration and net benefit using decision curve analysis were determined for each nomogram.

Results

A total of 437 patients were included, comprising 0.7% with low-risk disease, 39.8% with intermediate-risk disease, and 59.5% with high-risk disease. Among them, 86 out of 437 (19.7%) had pN1 disease. The sensitivity and specificity of PSMA PET/CT for the detection of LNI were 47.7% (95% CI: 36.8–58.7) and 95.4% (95% CI: 92.7–97.4), respectively. Among predictive models, the Amsterdam-Brisbane-Sydney model achieved the highest discrimination (AUC: 0.81, 95% CI: 0.76–0.86), followed by Muehlematter Model 1 (AUC: 0.79, 95% CI: 0.74–0.85), both with good calibration but slight systematic overestimation of risks across all thresholds. The MSKCC and Briganti 2012 models had AUCs of 0.68 (95% CI: 0.61–0.74) and 0.67 (95% CI: 0.61–0.73), respectively, and both had moderate calibration. Decision curve analysis indicated that the Amsterdam-Brisbane-Sydney model provided superior net benefit across thresholds of 5–20%, followed by the Muehlematter Model 1 nomogram showing benefit in the 14–20% range. Using thresholds of 8% for the Amsterdam-Brisbane-Sydney nomogram and 15% for Muehlematter Model 1, ePLND could be spared in 15% and 16% of patients, respectively, without missing any LNI cases.

Conclusion

External validation of the Muehlematter Model 1 and Amsterdam-Brisbane-Sydney nomograms for predicting LNI confirmed their strong model discrimination, moderate calibration, and good clinical utility, supporting their reliability as tools to guide clinical decision-making.