How Does the Delta-Radiomics Better Differentiate Pre-Invasive GGNs From Invasive GGNs?

Purpose: This study aimed to explore the role of delta-radiomics in differentiating pre-invasive ground-glass nodules (GGNs) from invasive GGNs, compared with radiomics signature. Materials and Methods: A total of 464 patients including 107 pre-invasive GGNs and 357 invasive GGNs were embraced in radiomics signature analysis. 3D regions of interest (ROIs) were contoured with ITK software. By means of ANOVA/MW, correlation analysis, and LASSO, the optimal radiomic features were selected. The logistic classifier of radiomics signature was constructed and radiomic scores (rad-scores) were calculated. A total of 379 patients including 48 pre-invasive GGNs and 331 invasive GGNs with baseline and follow-up CT examinations before surgeries were enrolled in delta-radiomics analysis. Finally, the logistic classifier of delta-radiomics was constructed. The receiver operating characteristic curves (ROCs) were built to evaluate the validity of classifiers. Results: For radiomics signature analysis, six features were selected from 396 radiomic features. The areas under curve (AUCs) of logistic classifiers were 0.865 (95% CI, 0.823-0.900) in the training set and 0.800 (95% CI, 0.724-0.863) in the testing set. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. As the follow-up interval went on, more and more delta-radiomic features became statistically different. The AUC of the delta-radiomics logistic classifier was 0.901 (95% CI, 0.867-0.928), which was higher than that of the radiomics signature. Conclusion: The radiomics signature contributes to distinguish pre-invasive and invasive GGNs. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. More and more delta-radiomic features appeared to be statistically different as follow-up interval prolonged. Delta-radiomics is superior to radiomics signature in differentiating pre-invasive and invasive GGNs.