To investigate the value of a nomogram based on non-contrast CT radiomics and morphological features in preoperatively differentiating lung adenocarcinoma from benign pulmonary nodules.

A total of 248 patients with pulmonary nodules admitted to our hospital from January 2018 to December 2024 were retrospectively enrolled and randomly divided into a training set 173 cases and a validation set 75 cases at a 7︰3 ratio. Radiomics features were extracted from non-contrast CT images. The radiomics score (Rads) was calculated from selected features. Morphological features of the pulmonary nodules were also evaluated. Multivariate logistic regression was applied to identify independent risk factors, and a nomogram model was constructed. Model performance was assessed using area under the curve of receiver operating characteristic curves (AUC), confidence interval (CI), calibration curves, and decision curve analysis.

Among the 248 cases, there were 142 lung adenocarcinomas and 106 benign nodules. Eleven key radiomics features and two morphological features (shallow lobulation and vascular convergence) were selected. Multivariate logistic regression revealed that shallow lobulation (OR=3.342, P=0.002), vascular convergence (OR=2.229, P=0.040), and Rads (OR=3.347, P<0.001) were independent risk factors for differentiating lung adenocarcinoma from benign nodules. The nomogram constructed based on these variables achieved an AUC of 0.831 (95%CI: 0.767~0.884) with a sensitivity of 67.0% and specificity of 84.3% in the training set, and an AUC of 0.825 (95%CI: 0.721~0.903) with a sensitivity of 74.4% and specificity of 86.1% in the validation set. Calibration curves demonstrated good model calibration (Hosmer-Lemeshow test: P=0.9434 in the training set, P=0.6345 in the validation set), and decision curve analysis indicated satisfactory clinical net benefit.

The nomogram model incorporating the CT radiomics score, shallow lobulation, and vascular convergence demonstrates stable and robust diagnostic performance in preoperatively differentiating lung adenocarcinoma from benign nodules. It may serve as a non-invasive auxiliary tool to support individualized preoperative decision-making for patients with pulmonary nodules.