Open Access

Song Xue, Holger Einspieler, Sijie Wen, Dina Muin, Ana Antic Nikolic, Jan Baessler, Gero Kramer, Shahrokh F. Shariat, Constantin Lapa, Marcus Hacker, Sazan Rasul, Xiang Li

• Prostate-specific membrane antigen radioligand therapy (PSMA-RLT) has emerged as a promising treatment for metastatic castration-resistant prostate cancer (mCRPC). However, current patient selection methods – largely based on qualitative imaging criteria – may impede precision and efficacy of treatment. We aimed to evaluate the predictive value of quantitative imaging biomarkers derived from dual-tracer [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT, with a focus on concordant lesions. Methods Thirty-seven mCRPC patients from two institutions underwent [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT prior to receiving at least two cycles of [177Lu]Lu-PSMA therapy. An automated pipeline enabled lesion segmentation, dual-tracer image fusion, and extraction of quantitative features from concordant (PSMA + /FDG +) and non-concordant lesions. A decision tree model was developed on the Vienna cohort (n = 24) and validated on an independent cohort from Augsburg (n = 13). SHAP analysis was used to identifyProstate-specific membrane antigen radioligand therapy (PSMA-RLT) has emerged as a promising treatment for metastatic castration-resistant prostate cancer (mCRPC). However, current patient selection methods – largely based on qualitative imaging criteria – may impede precision and efficacy of treatment. We aimed to evaluate the predictive value of quantitative imaging biomarkers derived from dual-tracer [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT, with a focus on concordant lesions. Methods Thirty-seven mCRPC patients from two institutions underwent [68 Ga]Ga-PSMA-11 and [18F]F-FDG PET/CT prior to receiving at least two cycles of [177Lu]Lu-PSMA therapy. An automated pipeline enabled lesion segmentation, dual-tracer image fusion, and extraction of quantitative features from concordant (PSMA + /FDG +) and non-concordant lesions. A decision tree model was developed on the Vienna cohort (n = 24) and validated on an independent cohort from Augsburg (n = 13). SHAP analysis was used to identify key predictive features. Results The decision tree achieved 95.8% accuracy in the training cohort and 84.6% in external validation. SUVmean of concordant lesions was the most predictive features. Patients with SUVmean[PSMA Concordant] ≥ 12.1 g/mL were more likely to respond. Organ-specific analysis further identified high SUVmax in bone metastases as a negative prognostic marker. Conclusions Quantitative metrics from dual-tracer PET, particularly those characterizing concordant lesions, show promise for predicting response to PSMA-RLT. These preliminary findings highlight the potential to move beyond binary eligibility criteria toward a more nuanced, biomarker-driven approach to patient selection.…