Construction and validation of a prognostic nomogram for advanced esophageal squamous cell carcinoma patients treated with PD-1 inhibitor-based therapy.

This study aimed to develop a prognostic nomogram to enhance the accuracy of survival prediction and guide individualized treatment decisions for patients with advanced esophageal squamous cell carcinoma (ESCC). A total of 162 patients with advanced ESCC treated with PD-1 inhibitor-based therapy at the First Affiliated Hospital of Anhui Medical University constituted the training set, while 79 patients from the Second Affiliated Hospital formed the validation set. Independent prognostic factors associated with overall survival (OS) were identified using multivariate Cox regression analysis to construct the nomogram. The model’s performance was evaluated in terms of discrimination, calibration, generalizability and clinical utility through the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration plots, external validation and decision curve analysis (DCA). Kaplan-Meier analysis with Log-Rank tests was employed to compare OS across different risk strata. The nomogram incorporated five independent prognostic variables. In the training set, the C-index was 0.769, with AUC values of 0.942, 0.850, and 0.658 for predicting 0.5-, 1-, and 2-year OS, respectively. In the validation set, the C-index was 0.786, with corresponding AUC values of 0.916, 0.919, and 0.800. The nomogram demonstrated superior predictive performance compared to the neutrophil-to-lymphocyte ratio (NLR) based on both C-index and AUC metrics. Calibration plots showed good agreement between predicted and observed survival probabilities at 0.5, 1, and 2 years. DCA confirmed the satisfactory clinical utility of the model. Stratification analyses revealed significantly longer OS in low-risk patients compared to high-risk patients in both sets (all  < 0.001). This nomogram provides a reliable tool for predicting prognosis in patients with advanced ESCC undergoing PD-1 inhibitor-based therapy, offering a potential reference for clinical decision-making.