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Background <p>This study evaluates the utility of quantitative imaging biomarkers derived from whole-body diffusion-weighted MRI (WB-DWMRI) and [<sup>68</sup>Ga]GaPSMA-PET/CT in predicting lesion-level response to [<sup>177</sup>Lu]LuPSMA therapy in metastatic castration-resistant prostate cancer (mCRPC).</p> Methods <p>Twenty-two patients with mCRPC who underwent WB-DWMRI and [<sup>68</sup>Ga]GaPSMA-PET/CT within three months before [<sup>177</sup>Lu]LuPSMA therapy were identified. The PSMA SUV (SUV<sub>mean</sub>, SUV<sub>SD</sub>, SUV<sub>peak</sub>) and corresponding diffusion weighted imaging (DWI) parameters (ADC<sub>mean</sub>, ADC<sub>kurtosis</sub>, ADC<sub>vol</sub>) were extracted from five hottest lesions (highest SUV<sub>mean</sub>) 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.</p> Results <p>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<sub>mean</sub> and SUV<sub>peak</sub> 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<sub>mean</sub> (rank-correlation = 0.43). Neither ADC<sub>mean</sub> (AUROC = 0.45, <i>p</i> = 0.47) or ADC<sub>kurtosis</sub> (AUROC = 0.49, <i>p</i> = 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 (<i>p</i> = 0.06). A 1-unit SUV<sub>mean</sub> 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 (<i>p</i> &lt; 0.01). No evidence suggested combining volume with SUVmean enhanced predictive performance over SUV<sub>mean</sub> alone (<i>p</i> = 0.58).</p> Conclusions <p>Baseline PSMA SUV<sub>mean</sub>, SUV<sub>peak</sub>, and volume are promising predictive biomarkers for lesion-level response to [<sup>177</sup>Lu]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.</p>

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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)

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  • 发表日期：

    2026年6月30日

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  • Minal Padden-Modi,
  • Jan Taprogge,
  • Peter Dutey-Magni,
  • Carolina G. S. Cauduro,
  • Matthew D. Blackledge,
  • Hoda Abdel-Aty,
  • Iain Murray,
  • Nabil Hujairi,
  • Nina Tunariu,
  • Nick James

摘要

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 (SUVmean, SUVSD, SUVpeak) and corresponding diffusion weighted imaging (DWI) parameters (ADCmean, ADCkurtosis, ADCvol) 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 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. SUVmean and SUVpeak 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 ADCkurtosis (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 [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.