Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma.

Abstract

AIM\nThe purpose of this study was to investigate the relationship between epidermal growth factor receptor (EGFR) mutation status and computed tomography (CT) features in patients with lung adenocarcinoma.\n\n\nMATERIALS AND METHODS\nA total of 483 patients with lung adenocarcinoma diagnosed between January 2015 and April 2020 were included in this study. All patients underwent a preoperative chest CT, and a total of 31 detailed CT features were quantified. The mutation status of EGFR exon 18-21 was detected by a polymerase chain reaction (PCR)-based amplified refractory mutation system. Student s t and Fisher s exact or chi-square tests were used to compare continuous and categorical variables, respectively. Least absolute shrinkage and selection operator (LASSO) regularisation was used to determine the optimal combination of CT features and clinical characteristics to predict the EGFR mutation status. The model was tested using a validation set consisting of 120 patients.\n\n\nRESULTS\nEGFR mutations were found in 249 (51.6%) of 483 patients with lung adenocarcinoma. Univariate analysis showed that 14 CT features and two clinical characteristics correlated significantly with the EGFR mutation status. Smoking history, long-axis diameter, bubble-like lucency, pleural retraction, thickened bronchovascular bundles, and peripheral emphysema were independent predictors of the EGFR mutation status, according to LASSO regularisation. In the training and verification cohorts, the areas under the curve of the prediction model were 0.766 and 0.745, respectively.\n\n\nCONCLUSIONS\nCT features of patients with lung adenocarcinoma can help predict the EGFR mutation status.