Combining Computed Tomography Radiomics and Clinical Features to Predict Lymph Node Metastasis in Patients with Lung Cancer

Background This study aimed to develop a predictive model for lymph node metastasis in patients with lung cancer using non-contrast computed tomography (CT). Methods A total of 403 patients with lung cancer who met the inclusion criteria were randomly divided into training (n = 282) and test (n = 121) sets. Clinical information was collected, and radiomic features were extracted from non-contrast chest CT images using the “Radiomics” toolkit in 3D Slicer software. Subsequently, least absolute shrinkage and selection operator regression analysis was employed to reduce the number of variables and establish a prediction model for lymph node metastasis in patients with lung cancer based on non-contrast CT scans. The predictive performance and clinical utility of the model were evaluated using the area under the curve (AUC) and decision curve analysis, and Shapley additive explanations analysis was applied to enhance interpretability. Results Lymph node metastasis was present in 35.5% (143/403) of patients. Two clinical features and 16 radiomic features most strongly associated with lymph node metastasis were identified, and nine models were constructed. The receiver operating characteristic curves of the combined clinical–radiomic model demonstrated favorable predictive performance. The clinical–radiomic SVM model demonstrated the best performance in predicting lymph node status (AUC = 0.927 in the training set, 0.852 in the internal test set, and 0.812 in the external test set).Decision curve analysis indicated that the prediction model provided substantial clinical benefits. Conclusion The radiomics model based on non-contrast CT demonstrated good diagnostic performance in predicting lymph node metastasis in patients with lung cancer and may provide guidance for individualized targeted therapy.