Objective:The global incidence of lung cancer highlights the need for improved assessment of nodule characteristics to enhance early detection of lung
adenocarcinoma presenting as ground-glass nodules (GGNs). This study investigated the applicability of radiomics features of vascular structures
within GGNs for predicting invasiveness of GGNs.Methods:In total, 165 pathologically confirmed pulmonary GGNs were retrospectively analyzed. The nodules were classified into preinvasive and invasive
groups and randomly categorized into training and validation sets in a 7:3 ratio. Four models were constructed and evaluated: radiomics-GGN,
radiomics-vascular, clinical-radiomics-GGN, and clinical-radiomics-vascular. The predictive performance of these models was assessed using
receiver operating characteristic curves, decision curve analysis, calibration curves, and DeLong’s test.Results:Significant differences were observed between the preinvasive and invasive groups in terms of age, nodule length, average diameter, morphology,
and lobulation sign (P = 0.006, 0.038, 0.046, 0.049, and 0.002, respectively). In the radiomics-GGN model, the support vector machine (SVM)
approach outperformed logistic regression (LR), achieving an area under the curve (AUC) of 0.958 in the training set and 0.763 in the validation
set. Similarly, in the radiomics-vascular model, the SVM approach outperformed LR. Furthermore, the clinical-radiomics-vascular model
demonstrated superior predictive performance compared with the clinical-radiomics-GGN model, with an AUC of 0.918 in the training set and
0.864 in the validation set. DeLong’s test indicated significant differences in predicting the invasiveness of pulmonary nodules between the
clinical-radiomics-vascular model and the clinical-radiomics-GGN model, both in the training and validation sets (P < 0.01).Conclusion:The radiomics models based on internal vascular structures of GGNs outperformed those based on GGNs alone, suggesting that incorporating
vascular radiomics analysis can improve the noninvasive assessment of GGN invasiveness, thereby aiding in clinical decision-making and guiding
biopsy selection and treatment planning.