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The prognostic role of staging [18F]PSMA-1007 PET/CT volumetric and dissemination features in prostate cancer

Domenico Albano, Alessandro Temponi, Francesco Bertagna, Nazareno Suardi, Anna Talin, Marco Lorenzo Bonù, Luca Triggiani

2025Annals of Nuclear Medicine6 citationsDOIOpen Access PDF

Abstract

Abstract Background This study aimed the role of volumetric and dissemination features of staging [18F]PSMA-1007 PET/CT in predicting progression‐free survival (PFS) in patients with prostate cancer (PCa) and their relationship with the main clinical data (ISUP grade groups, number of lesions, PSA). Methods We included 164 patients with high-risk PCa who underwent baseline [18F]PSMA-1007 PET/CT. With the help of LIFEx version 7.7, the main volumetric and dissemination PET parameters were semi-automatically extracted: PSMA-prostate tumor volume (PSMA-TV), PSMA-prostate total lesion (PSMA-TL), PSMA total TV (PSMA-TTV), PSMA total TL (PSMA-TTL) and Dmax corrected for body-surface-area (Dmax bsa ). Spearman rank correlations between semiquantitative PET features and the clinical variables were analyzed. PFS estimates were plotted with the Kaplan–Meier method. Results A high correlation was seen between the number of lesions and both PSMA-TTL (r 0.725), and Dmaxbsa (r 0.935). A moderate correlation was registered between PSA and PSMA-TTV (r 0.333), PSMA-TTL (r 0.441), Dmax bsa (r 0.333), as well as between number of lesions and PSMA-TTV (r 0.342). After a median follow-up of 17 months (range 2–45), relapse/progression happened in 17 patients (10%). PSA level, presence of distant metastases at staging, PSMA-TV, PSMA-TL, PSMA-TTL and Dmax bsa were significantly associated with PFS at univariate analysis, but only the presence of distant metastases, PSMA-TTL and Dmax bsa were confirmed to be independent prognostic factors. Conclusion Volumetric and dissemination features derived by staging [18F]PSMA-1007 PET/CT were significantly correlated with PSA and number of lesions. The combination of PSMA-TTL and Dmax bsa was the best predictor of PFS and may help to better stratify PCa patients.

Topics & Concepts

MedicineProstate cancerNuclear medicineProstateUnivariate analysisRadiologyOncologyInternal medicineCancerMultivariate analysisProstate Cancer Treatment and ResearchProstate Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging