Construction of a postoperative disease-free survival prediction model for non-small cell lung cancer patients based on dual-energy computed tomography parameters and blood inflammatory indicators

Background: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related death worldwide, and a significant proportion of patients undergoing curative surgery experience recurrence. The tumor node metastasis (TNM) staging system, while foundational, has limited precision for individualized recurrence risk prediction. Emerging biomarkers, including quantitative parameters from dual-energy computed tomography (DECT) and systemic inflammatory indices, have shown promising yet isolated prognostic value. However, a comprehensive model integrating these multimodal data to personalize recurrence risk assessment is lacking. This study aimed to construct a nomogram that integrates DECT quantitative parameters, blood inflammatory indicators, and clinical characteristics to predict disease-free survival (DFS) at 1, 2, and 3 years after surgery in patients with resectable NSCLC. Methods: A retrospective study included 140 patients with pathologically confirmed NSCLC who underwent DECT examination within 2 weeks before surgery, randomly assigned to a training set of 98 cases and a test set of 42 cases in a 7:3 ratio. Clinical characteristics, DECT quantitative parameters, and blood test indicators were collected. Potential predictive variables were identified through least absolute shrinkage and selection operator (LASSO) regression analysis, while independent risk factors were established using both univariate and multivariate Cox proportional hazards regression models, leading to the construction of the comprehensive nomogram model. The model's efficacy was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and C-index, with stratified survival analysis performed using the Kaplan-Meier method. Results: (>8.0), and high NLR (≥2.7) significantly shortened postoperative DFS (all P