Predictive imaging biomarkers on whole-body diffusion-weighted MRI (WB-DWMRI) and [68Ga]GaPSMA-PET/CT for [177Lu]LuPSMA therapy in metastatic prostate cancer (mCRPC).

Abstract

Background: This study evaluates the utility of quantitative imaging biomarkers derived from whole-body diffusion-weighted MRI (WB-DWMRI) and [⁶⁸Ga]GaPSMA-PET/CT in predicting lesion-level response to [¹⁷⁷Lu]LuPSMA therapy in metastatic castration-resistant prostate cancer (mCRPC).

Methods: Twenty-two patients with mCRPC who underwent WB-DWMRI and [⁶⁸Ga]GaPSMA-PET/CT within three months before [¹⁷⁷Lu]LuPSMA therapy were identified. The PSMA SUV (SUV_mean, SUV_SD, SUV_peak) and corresponding diffusion weighted imaging (DWI) parameters (ADC_mean, ADC_kurtosis, ADC_vol) were extracted from five hottest lesions (highest SUV_mean) on PSMA-PET/CT. Lesion response was assessed using modified PERCIST and MET-RADS-P criteria. Multilevel logistic regression and area under receiver operating characteristic (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 SUV_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 SUV_mean (rank-correlation = 0.43). Neither ADC_mean (AUROC = 0.45, p = 0.47) or ADC_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 SUV_mean 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 SUV_mean alone (p = 0.58).

Conclusions: Baseline PSMA SUV_mean, SUV_peak, and volume are promising predictive biomarkers for lesion-level response to [¹⁷⁷Lu]LuPSMA 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.