Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma.

ABSTRACT: Purpose: To develop and validate a radiomic signature to identify EGFR mutations in patients with advanced lung adenocarcinoma. Methods: This study involved 201 patients with advanced lung adenocarcinoma (140 in the training cohort and 61 in the validation cohort). A total of 396 features were extracted from manual segmentation based on enhanced and non-enhance CT imaging after image preprocessing. The Lasso algorithm was used for feature selection, 6 machine learning methods were used to construct radiomics models. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the performance of the radiomic signature between different data and methods. A nomogram was developed using clinical factors and the radiomics signature, then it was analyzed based on its discrimina

SUBMITTER: Hong D 

PROVIDER: S-EPMC7005234 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature