Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature.

ABSTRACT: Objective: To explore 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: A total of 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 regressions were used to establish a 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 was assessed

SUBMITTER: Zheng H 

PROVIDER: S-EPMC8770987 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature