Methods: In this multicentre retrospective study, we used data from patients of any age with histologically proven prostate cancer who underwent PSMA-PET at the University Hospitals in Essen, Münster, Freiburg, and Dresden, Germany, between Oct 30, 2014, and Dec 27, 2021. We linked a subset of patient hospital records with patient data, including mortality data, from the Cancer Registry North-Rhine Westphalia, Germany. Patients from Essen University Hospital were randomly assigned to the development or internal validation cohorts (2:1). Patients from Münster, Freiburg, and Dresden University Hospitals were included in an external validation cohort. Using the development cohort, we created quantitative and visual PPP nomograms based on Cox regression models, assessing potential PPP predictors for overall survival, with least absolute shrinkage and selection operator penalty for overall survival as the primary endpoint. Performance was measured using Harrell's C-index in the internal and external validation cohorts and compared with established clinical risk scores (International Staging Collaboration for Cancer of the Prostate [STARCAP], European Association of Urology [EAU], and National Comprehensive Cancer Network [NCCN] risk scores) and a previous nomogram defined by Gafita et al (hereafter referred to as GAFITA) using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) estimates.
Findings: We analysed 2414 male patients (1110 included in the development cohort, 502 in the internal cohort, and 802 in the external validation cohort), among whom 901 (37%) had died as of data cutoff (June 30, 2023; median follow-up of 52·9 months [IQR 33·9–79·0]). Predictors in the quantitative PPP nomogram were locoregional lymph node metastases (molecular imaging N2), distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases), tumour volume (in L), and tumour mean standardised uptake value. Predictors in the visual PPP nomogram were distant metastases (extrapelvic nodal metastases, bone metastases [disseminated or diffuse marrow involvement], and organ metastases) and total tumour lesion count. In the internal and external validation cohorts, C-indices were 0·80 (95% CI 0·77–0·84) and 0·77 (0·75–0·78) for the quantitative nomogram, respectively, and 0·78 (0·75–0·82) and 0·77 (0·75–0·78) for the visual nomogram, respectively. In the combined development and internal validation cohort, the quantitative PPP nomogram was superior to STARCAP risk score for patients at initial staging (n=139 with available staging data; AUC 0·73 vs 0·54; p=0·018), EAU risk score at biochemical recurrence (n=412; 0·69 vs 0·52; p<0·0001), and NCCN pan-stage risk score (n=1534; 0·81 vs 0·74; p<0·0001) for the prediction of overall survival, but was similar to GAFITA nomogram for metastatic hormone-sensitive prostate cancer (mHSPC; n=122; 0·76 vs 0·72; p=0·49) and metastatic castration-resistant prostate cancer (mCRPC; n=270; 0·67 vs 0·75; p=0·20). The visual PPP nomogram was superior to EAU at biochemical recurrence (n=414; 0·64 vs 0·52; p=0·0004) and NCCN across all stages (n=1544; 0·79 vs 0·73; p<0·0001), but similar to STARCAP for initial staging (n=140; 0·56 vs 0·53; p=0·74) and GAFITA for mHSPC (n=122; 0·74 vs 0·72; p=0·66) and mCRPC (n=270; 0·71 vs 0·75; p=0·23).
Madeleine J Karpinski MSc,1,2,3 Johannes Hüsing PhD,1 Kevin Claassen PhD1,4 Lennart Möller MPH,1 Hiltraud Kajüter MSc,1 Florian Oesterling MSc,1 Prof Viktor Grünwald MD,5,6 Prof Lale Umutlu MD,7 Prof Jens Kleesiek PhD,8 Tugce Telli MD,2 Anja Merkel-Jens MD,9 Anika Hüsing PhD,9 Claudia Kesch MD,5 Prof Ken Herrmann MD,2 Prof Matthias Eiber MD,10 Sebastian Hoberück MD,11 Prof Philipp T Meyer MD PhD,12 Felix Kind MD,12 Prof Kambiz Rahbar MD,3 Prof Michael Schäfers MD,3 Prof Andreas Stang MD MPH,1,9 Prof Boris A Hadaschik MD,5 Prof Wolfgang P Fendler MD,2
- Cancer Registry North-Rhine Westphalia, Bochum, Germany
- Department of Nuclear Medicine, DKTK and NCT University Hospital Essen, Essen, Germany
- Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
- Department of Medical Statistics and Epidemiology, Medical School Hamburg, Germany
- Department of Urology, University Hospital Essen, Essen, Germany
- Department for Medical Oncology, University Hospital Essen, Essen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
- Institute for AI in Medicine, University Hospital Essen, Essen, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany
- Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Department of Nuclear Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany