Predicting EGFR mutation status in lung adenocarcinoma: development and validation of a computed tomography-based radiomics signature.

ABSTRACT: Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma can benefit from targeted therapy. However, noninvasively determination of EGFR mutation status before targeted therapy remains a challenge. This study constructed a nomogram based on a combination of radiomics features with the clinical and radiological features to predict the EGFR mutation status. The least absolute shrinkage and selection operator (LASSO) and Wilcoxon test were used for feature selection. Decision tree (DT), logistic regression (LR), and support vector machine (SVM) classifiers were used for radiomics model building. Used the clinical and radiological features establish clinical-radiology (C-R) model. The C-R model with the best radiomics model to establish clinical-radiological-radio

SUBMITTER: Zhang G 

PROVIDER: S-EPMC7868761 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature