Development and validation of a novel nomogram model for predicting postoperative survival of T4N0M0 NSCLC: a population-based survival analysis

Objective: The risk factors of postoperative survival in T4N0M0 NSCLC patients are not fully understood. This study aimed to develop and validate a nomogram model for predicting postoperative survival in patients with T4N0M0 non-small cell lung cancer (NSCLC). Methods: Clinicopathological data of patients were collected from Surveillance, Epidemiology, and End Results (SEER) database. Priori clinical prognostic variables were identified via univariate and multivariate Cox proportional hazards models, and nomograms were constructed to predict 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS). The model was assessed employing the area under a time-dependent receiver operating characteristic curve (AUC), the concordance index (C-index) and the calibration curve. Results: A total of 2,326 patients who underwent radical resection for T4N0M0 NSCLC between 2010 and 2021 were retrospectively included. The nomograms model established based on priori clinical prognostic variables demonstrated good discriminative performance, with C-index values of 0.712 (OS) and 0.726 (CSS) in the training cohort, and 0.704 (OS) and 0.738 (CSS) in the validation cohort. Calibration curves showed agreement between predicted and observed survival outcomes, while decision curve analysis confirmed the clinical utility of the models. Survival stratification revealed significant differences between high- and low-risk groups (median OS: 113 vs. 46 months; median CSS: 109 vs. 44 months, both P<0.001). Conclusion: This study established nomograms which provides a evidence-based tool for prognostic assessment of T4N0M0 NSCLC patients, enabling risk stratification and guiding tailored treatment decisions.