1. Baseline Imaging Derived Predictive Factors of Response Following [177Lu]Lu-PSMA-617 Therapy in Salvage Metastatic Castration-Resistant Prostate Cancer: A Lesion- and Patient-Based Analysis
- Author
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Esmée C. A. van der Sar, Adinda J. S. Kühr, Sander C. Ebbers, Andrew M. Henderson, Bart de Keizer, Marnix G. E. H. Lam, and Arthur J. A. T. Braat
- Subjects
prostate cancer ,lutetium ,radio-ligand therapy ,prostate specific membrane antigen ,predictors ,Biology (General) ,QH301-705.5 - Abstract
Earlier studies have mostly identified pre-therapeutic clinical and laboratory parameters for the prediction of treatment response to [177Lu]Lu-PSMA-617 in metastatic castration resistant prostate cancer patients (mCRPC). The current study investigated whether imaging-derived factors on baseline [68Ga]Ga-PSMA-11 PET/CT can potentially predict the response after two cycles of [177Lu]Lu-PSMA-617 treatment, in a lesion- and patient-based analysis in men with mCRPC. Included patients had histologically proven mCRPC and a [68Ga]Ga-PSMA-11 PET/CT before and after two cycles of [177Lu]Lu-PSMA-617 treatment. The imaging-based response was evaluated on lesion-level (standardized uptake value (SUV) reduction) and patient-level (total lesion PSMA (TL-PSMA) reduction). In the lesion-level analysis, a clear relationship was found between SUVpeak/max and the imaging-based response to [68Ga]Ga-PSMA-11 PET/CT (most avid lesion SUVpeak/max ≥ 30% reduction) (p < 0.001), with no significant difference in cut-off values between different sites of metastases (i.e., lymph node, bone or visceral metastasis). In patient-level analysis, baseline PSA and SUVpeak values of most avid metastasis were significantly associated with imaging-based response (TL-PSMA ≥ 30% reduction) (p = 0.019 and p = 0.015). In pre-treatment with [68Ga]Ga-PSMA-11 PET/CT, a clear accumulation-response relationship in lesion-level was found for SUVpeak/max in men with mCRPC receiving two cycles of [177Lu]Lu-PSMA-617 treatment. The SUVpeak of the most avid lesion was the only image-derived factor predictive of the imaging-based response at the patient-level.
- Published
- 2022
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