Predictive value of quantitative parameters from dual-tracer FDG and PSMA PET/CT for progression to castration resistance in patients with metastatic hormone-sensitive prostate cancer.

[PURPOSES] To explore the prognostic value of dual-tracer PET/CT-derived parameters and develop a predictive model for castration-resistant prostate cancer-free survival (CRPC-FS) in patients with metastatic hormone-sensitive prostate cancer (mHSPC).

[METHODS] Forty-five mHSPC patients who underwent prostate-specific membrane antigen (PSMA) and fluorodeoxyglucose (FDG) PET/CT prior to novel hormone therapy (NHT) were included between October 2020 and March 2024. A total of 1,661 PSMA-positive and 303 FDG-positive lesions were evaluated to quantify tumor burden, including maximum standardized uptake value, metabolic tumor volume (MTV) and total lesion uptake under different relative fixed threshold values. Cox regression and Kaplan-Meier analyses assessed their association with CRPC-FS. Internal validation was performed using 1000-bootstrap resampling. Classification and regression tree identified optimal cut-offs. Time-dependent receiver operating characteristic analysis evaluated model discrimination.

[RESULTS] In univariate analysis, only MTV of prostate cancer was significantly associated with CRPC-FS (p < 0.05). PSMA-derived (C-index = 0.662) MTV and FDG-derived (C-index = 0.712) MTV under a 40 % threshold exhibited the strongest predictive power and remained independent in multivariable analysis. Internal validation demonstrated favorable model performance and good calibration. The cut-off values for the two parameters were identified as 4.77 and 66.425, respectively. Furthermore, patients were stratified into prognostic groups to develop a risk model with AUCs > 0.85.

[CONCLUSIONS] MTV exhibited superior prognostic value for CRPC-FS in patients with mHSPC compared to other PET/CT-derived and clinical parameters. Moreover, FDG-derived MTV showed stronger prognostic value than PSMA-derived MTV. The dual-tracer PET/CT-derived model could comprehensively assess tumor burden and aid in early prognostic stratification.