A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.

ABSTRACT: Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consu

SUBMITTER: Yao Y 

PROVIDER: S-EPMC11536698 | biostudies-literature | 2024 Oct-Dec

REPOSITORIES: biostudies-literature