The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

Abstract

Abstract Objective To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules. Materials and methods This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (ModelAP, ModelVP and ModelCombination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort. Results A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of ModelAP, ModelVP and ModelCombination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682–0.948), 0.7485 (95% CI 0.602–0.895), and 0.8772 (95% CI 0.780–0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between ModelAP and ModelCombination (P = 0.0396) and between ModelVP and ModelCombination (P = 0.0465). However, the difference in AUCs between ModelAP and ModelVP was not significant (P = 0.5061). These results demonstrate that ModelCombination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model. Conclusions We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

Keywords

• Pulmonary nodules
• Computed tomography
• Dual-energy
• Radiomics