Development and Validation of a Predictive Nomogram for Early Postoperative Recurrence in Non-Small Cell Lung Cancer

BACKGROUND Early postoperative recurrence in non-small cell lung cancer (NSCLC) significantly impacts survival outcomes, yet current staging systems often fail to accurately predict recurrence risk. In this study, we analyzed the risk factors associated with early postoperative recurrence in NSCLC and developed a nomogram model to enhance individualized risk assessment. MATERIAL AND METHODS A retrospective analysis was conducted on data from 286 NSCLC patients who underwent R0 resection between January 2020 and December 2022, categorized into a control group (n=235) and an observation group (n=51). Demographic and clinicopathological data were collected, and multivariate logistic regression was used to identify independent risk factors. A nomogram was developed and validated using internal resampling. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration performance was evaluated using the Hosmer-Lemeshow test and calibration curves. RESULTS Multivariate analysis identified elevated D-dimer levels (≥0.5 mg/L), pleural invasion, lymphovascular invasion, and lymph node metastasis as independent predictors of early recurrence. The nomogram achieved an AUC of 0.806, with a sensitivity of 78.9% and specificity of 80.1%. Internal validation yielded a corrected C-index of 0.689. Calibration curves demonstrated good agreement between predicted and observed recurrence probabilities. Decision curve analysis showed the model provided a higher net benefit compared to extreme intervention strategies. CONCLUSIONS Elevated D-dimer levels, pleural invasion, lymphovascular invasion, and lymph node metastasis were key risk factors predicting early postoperative recurrence in NSCLC. The nomogram demonstrated strong predictive accuracy and calibration, offering a useful tool for individualized risk assessment.