To evaluate the value of quantitative parameters and radiomic features based on dual-layer spectral detector CT (DLCT) in predicting spread through air spaces (STAS) of lung adenocarcinoma (LUAD). This study analyzed 266 patients with pathologically confirmed LUAD from two medical centers. Patients from center 1 were divided into training ( n = 131) and internal validation ( n = 57) sets, while center 2 ( n = 78) formed the external validation set. Clinical data, conventional imaging features, and DLCT quantitative parameters were analyzed to develop a clinical-radiological model. Radiomic features were extracted from venous-phase images, including conventional images, virtual monoenergetic images (VMI) at 40 keV, 65 keV, and 100 keV, along with iodine density maps, effective atomic number (Zeff) maps, and electron density (ED) maps. The best-performing radiomics model was combined with clinical-radiological predictors to create a nomogram. Model performance was evaluated through ROC analysis, calibration curves, and decision curve analysis. Multivariate analysis revealed that tumor-lung interface and ED values were independent predictive factors in the clinical-radiological model. The optimal radiomics model was constructed based on VMI 40 keV, demonstrating AUCs of 0.899, 0.835, and 0.828 in the training, internal validation, and external validation sets, respectively. The nomogram, which incorporated the VMI 40 keV radiomics signature along with tumor-lung interface and ED values, outperformed the clinical-radiological model in the training set (AUC = 0.910 vs. 0.870; P = 0.018) and the internal validation set (AUC = 0.868 vs. 0.798; P = 0.046). While the improvement in the external validation set was not statistically significant (AUC = 0.848 vs. 0.819; P = 0.184). The nomogram, which integrates conventional imaging features, DLCT quantitative parameters and VMI 40 keV radiomic features, demonstrates promising performance and represents a potential valuable non-invasive tool for the preoperative assessment of STAS in LUAD.
Huang et al. (Mon,) studied this question.