Quantitative PSMA PET Biomarkers for Predicting Response to ¹⁷⁷ Lu-PSMA Therapy in Prostate Cancer: A Systematic Review and Meta-analysis

¹⁷⁷Lu-labeled prostate-specific membrane antigen (PSMA) radiopharmaceutical therapy (¹⁷⁷Lu-PSMA-617) has emerged as a promising targeted therapy for metastatic castration-resistant prostate cancer (mCRPC). However, response rates to therapy vary among patients. Quantitative PSMA PET biomarkers have been increasingly explored for their prognostic potential in guiding patient selection. This systematic review and meta-analysis aims to evaluate the prognostic value of PSMA PET biomarkers for response to ¹⁷⁷Lu-PSMA therapy in patients with mCRPC. <b>Methods:</b> A systematic search was conducted across PubMed, Scopus, Web of Science, and Embase databases to identify studies evaluating quantitative PSMA PET parameters as predictors of response to ¹⁷⁷Lu-PSMA therapy. Hazard ratios (HRs) and 95% CIs for quantitative PSMA PET parameters were extracted and synthesized using random-effects meta-analysis. Heterogeneity was assessed using <i>I</i> ² statistics, and publication bias was evaluated via funnel plots and the Egger test. <b>Results:</b> A total of 23 studies met the inclusion criteria. Among PSMA PET parameters, SUV_mean, tumor volume (TV), or total lesion (TL) PSMA emerged as significant predictors of overall survival (OS), progression free survival, and prostate-specific antigen response. The pooled HR for SUV_mean in predicting OS was 0.93 (95% CI, 0.90-0.96; <i>I</i> ² = 92.8%), whereas TV showed a HR of 1.37 (95% CI, 1.27-1.47; <i>I</i> ² = 4.7%), and the TL had a HR of 1.04 (95% CI, 1.03-1.05; <i>I</i> ² = 16.1%). Additionally, SUV_max and SUV_peak demonstrated limited predictive value. <b>Conclusion:</b> Our findings indicate that SUV_mean, TV, and TL derived from PSMA PET imaging could serve as valuable biomarkers for prognosticating response to ¹⁷⁷Lu-PSMA therapy in patients with mCRPC. Future studies should aim to integrate PET biomarkers with clinical and biochemical parameters to improve treatment decision-making.