Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature - Zheng, Hui; Zhang, Hanfei; Wang, Shan; Xiao, Feng; Liao, Meiyan (Frontiers in Genetics, 2022)
Objective: To explored the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). Methods: 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n=219) and test set (n=93). Univariate and multivariate logistic regression were used to establish clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models were assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index. Results: Diameter (odds ratio [OR], 1.159; P＜0.001), lobulation (OR, 2.953; P=0.002), and vascular changes (OR, 3.431; P＜0.001) were retained as independent predictors of invasive adenocarcinoma (IAC) group. 11 radiomics features were selected by mRMR and LASSO method to established radiomics model. Clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs 0.83 vs 0.82; test set: 0.80 vs 0.78 vs 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score With a C-index of 0.855 (95%: 0.805~0.905). Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, served as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.