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 - EAU-YAU Prostate Canc Working Party
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 - 2025/4/2
Y1 - 2025/4/2
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.
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=eur_pure&SrcAuth=WosAPI&KeyUT=WOS:001457961500001&DestLinkType=FullRecord&DestApp=WOS_CPL
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 -
