Fully automated volumetric assessment of tumor burden using artificial intelligence on <sup>68</sup>Ga-PSMA-11 PET predicts survival after <sup>177</sup>Lu-PSMA therapy in metastatic Castration-resistant prostate cancer.
Authors
Affiliations (6)
Affiliations (6)
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China.
- United Imaging Healthcare, Shanghai, China.
- Department of Urology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, 21287, MD, USA. [email protected].
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, 321 Zhongshan Rd, Nanjing, China. [email protected].
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 68 Changle Rd, Nanjing, China. [email protected].
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<sub>TV</sub>), total tumor load (PSMA<sub>TU</sub> = PSMA<sub>TV</sub> × SUV<sub>mean</sub>), and total tumor quotient (PSMA<sub>TQ</sub> = PSMA<sub>TV</sub> / SUV<sub>mean</sub>). A prognostic nomogram was developed through Cox regression analysis, incorporating LASSO regularization for variable selection. Univariate analysis revealed that higher PSMA<sub>TV</sub> (HR 1.26), PSMA<sub>TU</sub> (HR 1.18), and PSMA<sub>TQ</sub> (HR 1.29) were significantly associated with shorter overall survival (OS). A prognostic nomogram that integrated PSMA<sub>TQ</sub> 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<sub>TQ</sub> with clinical factors might help in personalized risk stratification, facilitating AI-aided therapeutic decision-making.