A Triple-Classification for The Evaluation of Lung Nodules Manifesting as Pure Ground-Glass Sign: A CT-Based Radiomic Analysis

Abstract

Objectives: To construct a noninvasive radiomics model for evaluation of pathologic degree and individually treated strategy for patients with the manifestation of imageology-ground glass nodules (GGNs) on CT images.Methods: The retrospective primary cohort investigation included the patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semi-automatically and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, Random Forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.Results: In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Four intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.951 (0.953 for AAH/AIS, 0.934 for MIA, 0.965 for IAC, in the training set) and 0.935 (0.938 for AAH/AIS, 0.940 for MIA, 0.926 for IAC, in the validation set) for evaluation of the GGNs.Conclusion: The triple classification based on intra- and peritumoral radiomics features derived from the non-contrast CT images had a satisfactory performance, which may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing individualized treatment strategy.