Fully automated volumetric assessment of tumor burden using artificial intelligence on ⁶⁸Ga-PSMA-11 PET predicts survival after ¹⁷⁷Lu-PSMA therapy in metastatic Castration-resistant prostate cancer.

Abstract

Despite the rapid development of artificial intelligence (AI)-powered automated segmentation tools for PET/CT imaging, their prognostic value in predicting survival outcomes remains inadequately assessed. Our objective was to explore the prognostic significance of tumor burden quantification derived from PSMA PET/CT using AI for metastatic castration-resistant prostate cancer (mCRPC) patients receiving Lutetium-177 (¹⁷⁷Lu) PSMA therapy. A retrospective cohort of 107 consecutive patients with mCRPC treated with ¹⁷⁷Lu-PSMA therapy were analyzed. Utilizing a deep learning algorithm, PSMA-positive lesions were automatically delineated on baseline 68Ga-PSMA-11 PET/CT scans. Key metrics were derived from the segmented lesions: total tumor volume (PSMA_TV), total tumor load (PSMA_TU = PSMA_TV × SUV_mean), and total tumor quotient (PSMA_TQ = PSMA_TV / SUV_mean). A prognostic nomogram was developed through Cox regression analysis, incorporating LASSO regularization for variable selection. Univariate analysis revealed that higher PSMA_TV (HR 1.26), PSMA_TU (HR 1.18), and PSMA_TQ (HR 1.29) were significantly associated with shorter overall survival (OS). A prognostic nomogram that integrated PSMA_TQ alongside chemotherapy history, hemoglobin levels, alkaline phosphatase, and prostate-specific antigen demonstrated a bootstrap-corrected C-index of 0.71 (95% CI 0.64-0.78). Risk stratification using the nomogram showed significantly prolonged OS in low-risk vs. high-risk groups (median OS 30.9 vs. 7.9 months; HR 0.25, 95% CI 0.13-0.45, P < 0.001). The retrospective design is a study limitation. AI-based volumetric analysis of tumor burden on PSMA PET has prognostic significance for survival in ¹⁷⁷Lu-PSMA-treated mCRPC patients. The nomogram integrating PSMA_TQ with clinical factors might help in personalized risk stratification, facilitating AI-aided therapeutic decision-making.