Predictive imaging biomarkers on Whole-Body Diffusion Weighted MRI (WB-DWMRI) and 68 Ga-PSMA-PET/CT for 177 Lu-PSMA-Therapy in metastatic prostate cancer (mPC)

Purpose This study evaluates the utility of quantitative imaging biomarkers derived from WB-DWMRI and ⁶⁸ Ga-PSMA-PET/CT in predicting lesion-level response to ¹⁷⁷ Lu-PSMA-Therapy in mPC. Methods Twenty-two patients with mPC who underwent WB-DWMRI and ⁶⁸ Ga-PSMA-PET/CT within three months before ¹⁷⁷ Lu-PSMA therapy were identified. The PSMA SUV (mean, standard-deviation, peak) and corresponding DW Imaging (DWI) parameters (ADC mean, ADC kurtosis, volume) were extracted from five hottest lesions (highest SUVmean) on PSMA-PET/CT. Lesion response was assessed using modified PERCIST and MET-RADS-P criteria. Multilevel logistic regression and area under receiver operating characteristics (AUROC) analyses identified predictive biomarkers. Results 65 bone lesions and 30 lymph nodes were analysed pre- and post-therapy. Forty-four (68%) bone and 16 (53%) lymph nodes lesions responded to treatment. SUV mean and peak were almost identical (rank-correlation = 0.97) and had high predictive performance for response with AUROC of 0.74 (95% CI: 0.62–0.86) and 0.75 (95% CI: 0.61–0.88) respectively. Lesion volume showed good performance (AUROC = 0.69, 95% CI 0.57–0.82) and moderately correlated with SUVmean (rank-correlation = 0.43). Neither ADCmean (AUROC = 0.45, p  = 0.47) or kurtosis (AUROC = 0.49, p = 0.171) were predictive. On regression modelling, a 1-unit volume increase, raised response odds by 4.3 (95% CI 0.7–27) in bone lesions, and 6.2 (95% CI 0.6–67) in lymph nodes ( p  = 0.06). A 1-unit SUVmean increase raised response odds by 1.5 (95% CI 1.1–2.0) in bone lesions and 1.2 (95% CI 1.0–1.4) in lymph nodes ( p  < 0.01). No evidence suggested combining volume with SUVmean enhanced predictive performance over SUVmean alone ( p  = 0.58). Conclusions Baseline PSMA SUVmean, SUVpeak, and volume are promising predictive biomarkers for lesion-level response to ¹⁷⁷ Lu-PSMA-Therapy. Combining functional and structural imaging biomarkers could improve treatment stratification and response assessment. Further validation in larger studies, alongside patient-level analysis and expansion beyond the five lesions is needed to refine predictive models for clinical application.