Open AccessClinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer
Author(s) -
Dang Yutao,
Wang Ruotian,
Qian Kun,
Lu Jie,
Zhang Haixiang,
Zhang Yi
Publication year - 2021
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.13107
Subject(s) - epidermal growth factor receptor , medicine , receiver operating characteristic , nomogram , oncology , mutation , lung cancer , logistic regression , biomarker , cancer , radiology , biology , biochemistry , gene
Abstract Purpose To determine the prognostic factors of epidermal growth factor receptor (EGFR) mutation status in a group of patients with nonsmall cell lung cancer (NSCLC) by analyzing their clinical and radiological features. Materials and methods Patients with NSCLC who underwent EGFR mutation detection between 2014 and 2017 were included. Clinical features and general imaging features were collected, and radiomic features were extracted from CT data by 3D Slicer software. Prognostic factors of EGFR mutation status were selected by least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model of EGFR mutation. Results A total of 118 patients were enrolled in this study. The smoking index ( P = 0.028), pleural retraction ( P = 0.041), and three radiomic features were significantly associated with EGFR mutation status. The areas under the ROC curve (AUCs) for prediction models of clinical features, general imaging features, and radiomic features were 0.284, 0.703, and 0.815, respectively, and the AUC for the combined prediction model of the three models was 0.894. Finally, a nomogram was established for individualized EGFR mutation prediction. Conclusions The combination of radiomic features with clinical features and general imaging features can enable discrimination of EGFR mutation status better than the use of any group of features alone. Our study may help develop a noninvasive biomarker to identify EGFR mutation status by using a combination of the three group features.