Background The prognostic value of the preoperative gamma-glutamyl transferase to lymphocyte ratio (GLR), an established marker in many solid tumors, remains unclear in non-muscle-invasive bladder cancer (NMIBC). This study aimed to investigate the significance of GLR for predicting recurrence in NMIBC patients after transurethral resection of bladder tumor (TURBt). Methods We retrospectively analyzed 254 patients with primary NMIBC who underwent TURBt from 2013 to 2024. Preoperative GLR was calculated from blood tests performed within one week of surgery. The primary endpoint was recurrence-free survival (RFS). The optimal GLR cutoff was determined using receiver operating characteristic (ROC) curve analysis. Kaplan-Meier method, log-rank tests, and Cox proportional hazards models were used to assess survival outcomes and identify independent prognostic factors. A novel prognostic nomogram for RFS was constructed and its performance was evaluated by concordance index (C-index), calibration curves, time-dependent ROC, and decision curve analysis (DCA). Results The optimal GLR cutoff was identified as 11.71. Patients with high GLR ( 11.71) had significantly poorer RFS (P 0.001). On multivariate analysis, a high GLR was an independent predictor of postoperative recurrence (Hazard Ratio (HR) = 2.822, 95% Confidence Interval (CI): 1.651–4.824, P 0.001). A nomogram incorporating GLR and established clinicopathological factors was developed. The inclusion of GLR significantly improved the model’s predictive accuracy, increasing the C-index from 0.745 to 0.785. The nomogram demonstrated good calibration and discrimination (3-year Area Under the Curve (AUC) = 0.72) and provided superior net clinical benefit in DCA. The prognostic value of GLR remained robust across all clinicopathological subgroups. Conclusion Preoperative GLR is a simple, cost-effective, and reliable independent biomarker for predicting recurrence in NMIBC patients following TURBt. The GLR-based nomogram integrates systemic inflammation with clinical risk factors, offering a more precise tool for individualized risk stratification. This model can help guide personalized follow-up strategies and adjuvant treatment decisions, holding significant potential for clinical application.
Zhang et al. (Tue,) studied this question.