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

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 (Model_AP, Model_VP and Model_Combination) 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 Model_AP, Model_VP and Model_Combination 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 Model_AP and Model_Combination (P = 0.0396) and between Model_VP and Model_Combination (P = 0.0465). However, the difference in AUCs between Model_AP and Model_VP was not significant (P = 0.5061). These results demonstrate that Model_Combination 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: Computed tomography; Dual-energy; Pulmonary nodules; Radiomics.

Fig. 1

Flow chart of the radiomics analysis steps. Two radiologists manually segmented the region of interest (ROI) of pulmonary nodules. For model construction, the radiomic features were extracted, and principal component analysis was performed. The area under the curve (AUC) of the review operating characteristic was used to assess the diagnostic accuracy of the models. Decision curve analysis was used to assess the clinical utility of the models

Fig. 2

Line graph of the cumulative contribution rates of various principal components after selection from primary radiomic features. A Model_AP, B Model_VP, C Model_Combination

Fig. 3

ROC curves of the AP radiomics model (blue line), VP radiomics model (red dotted line), and combined AP and VP radiomics model (purple line) in the discrimination of benign and malignant pulmonary nodules

Fig. 4

Decision curve analysis of the combined AP and VP model. The value of the y-axis represents the net benefit, and the x-axis represents the probability threshold. The results showed that when the threshold probability was within 0.06–0.50, the net benefit of the combined model was greater than that of the “all” and “none” schemes