Development and validation of an MRI-based clinical-radiomics nomogram for predicting lymph node metastasis in non-small cell lung cancer

To develop and validate a nomogram that integrates clinical factors and multiparametric MRI-based radiomics features for the preoperative prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC). This retrospective diagnostic accuracy study enrolled 220 patients with pathologically confirmed NSCLC (142 males; 60.77 ± 8.70 years) between September 2021 and October 2024. Patients were randomly divided into training and validation sets. A clinical model was constructed using independent predictors identified by univariable and multivariable logistic regression analysis. A radiomics signature was developed from T1WI, T2WI, and T1 mapping sequences using the least absolute shrinkage and selection operator logistic regression algorithm. A nomogram was developed by integrating the clinical model and the radiomics signature. Diagnostic performance was assessed by receiver operating characteristic analysis, calibration, and decision curve analysis. Two radiologists independently assessed LNM status in the validation set for comparison. Tumor maximum diameter and carcinoembryonic antigen level were identified as independent predictors for the clinical model. In the validation set, the nomogram achieved an area under the curve (AUC) of 0.847, significantly greater than the clinical model (AUC = 0.710, p = 0.033) and the radiomics signature alone (AUC = 0.802, p = 0.033). The AUC of the nomogram was significantly higher than two radiologists (0.847 vs. 0.682, p = 0.022; 0.847 vs. 0.698, p = 0.041, respectively). The nomogram could serve as a noninvasive tool for preoperative prediction of LNM in NSCLC, thereby aiding in clinical decision-making.