TY - JOUR
T1 - External validation of nomograms including PSMA PET information for the prediction of lymph node involvement of prostate cancer
AU - Van Bergen, Tessa D
AU - Braat, Arthur J A T
AU - Hermsen, Rick
AU - Heetman, Joris G
AU - Wever, Lieke
AU - Lavalaye, Jules
AU - Vinken, Maarten
AU - Bahler, Clinton D
AU - Tann, Mark
AU - Kesch, Claudia
AU - Telli, Tugce
AU - Chiu, Peter Ka-Fung
AU - Wu, Kwan Kit
AU - Zattoni, Fabio
AU - Evangelista, Laura
AU - Ceci, Francesco
AU - Miszczyk, Marcin
AU - Rajwa, Pawel
AU - Barletta, Francesco
AU - Gandaglia, Giorgio
AU - Van Basten, Jean-Paul A
AU - Scheltema, Matthijs J
AU - Van Melick, Harm H E
AU - Van den Bergh, Roderick C N
AU - Van den Berg, Cornelis A T
AU - Marra, Giancarlo
AU - Soeterik, Timo F W
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/8
Y1 - 2025/8
N2 - 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.
AB - 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.
KW - External validation
KW - Lymph node involvement
KW - Nomogram
KW - PSMA PET/CT
KW - Prostate cancer
UR - https://www.scopus.com/pages/publications/105002149406
U2 - 10.1007/s00259-025-07241-y
DO - 10.1007/s00259-025-07241-y
M3 - Article
C2 - 40172694
SN - 1619-7070
VL - 52
SP - 3744
EP - 3756
JO - European Journal of Nuclear Medicine and Molecular Imaging
JF - European Journal of Nuclear Medicine and Molecular Imaging
IS - 10
ER -