Abstract
Objective: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant.
Methods: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model.
Results: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model.
Conclusion: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.
