A Comprehensive Analysis of Volumetric 68Ga-PSMA PET/CT Parameters, Clinical and Histopathologic Features: Evaluation of the Predictive Role.

Objectives: To evaluate the relationships between volumetric ⁶⁸Ga-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) parameters, Gleason score (GS), prostate-specific antigen (PSA) levels, histopathological data, and metastatic status in newly diagnosed prostate cancer (PCa) patients and to assess the predictive factors for progression despite treatment.

Methods: A total of 78 newly diagnosed patients with PCa who had ⁶⁸Ga-PSMA PET/CT scans were included. Clinical parameters, histopathological data, and metastatic status were documented, and volumetric parameters of primary prostate lesions were measured. All obtained data were compared statistically.

Results: Primary prostate tumor maximum standardized uptake value (SUV_max) and GS were significantly related to serum PSA levels (p<0.05). PSA levels and SUV_max values were significantly higher in patients with lymph node metastases than in those without. GS was found to be significantly increased in metastatic patients. PSMA-derived tumor volume (PSMA-TV) and total lesion PSMA of the primary lesion had a significant relationship with PSA value, GS, and regional lymph node metastases. Receiver operating characteristic analysis, conducted in patients with metastatic and localized disease, identified the cutoff value for SUV_max as 10.85. According to the results of the logistic regression analysis, PSMA-TV was found to be a predictive factor for progression despite treatment.

Conclusion: ⁶⁸Ga-PSMA PET/CT remains an invaluable imaging modality that should be considered first in PCa staging because of its superior compatibility with clinical and histopathologic data. The importance of this method goes beyond diagnostic accuracy; it also extends into the predictive domain, where the PSMA-TV value of primary prostate lesions is a potential predictor of treatment efficacy. This information is valuable for personalizing patient treatment, improving prognostic accuracy, and predicting clinical outcomes.