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Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer
Author(s) -
Li Shu,
Ding Changwei,
Zhang Hao,
Song Jiangdian,
Wu Lei
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13747
Subject(s) - radiomics , epidermal growth factor receptor , lung cancer , univariate , medicine , logistic regression , oncology , cancer , radiology , machine learning , computer science , multivariate statistics
Purpose This retrospective study was designed to investigate the ability of radiomics to predict the mutation status of epidermal growth factor receptor (EGFR) subtypes (19Del and L858R) in patients with non‐small cell lung cancer (NSCLC). Methods In total, 312 patients with NSCLC were included, and 580 radiomic features were extracted from the computed tomography images of each patient. In the training set, univariate analysis was performed on the clinical and radiomic features; logistic regression models were established using a 5‐fold cross validation strategy for the prediction of EGFR subtypes 19Del and L858R. Subsequently, the predictive ability of the joint models was evaluated using the test set. Results The results revealed that the radiomic features specific for EGFR 19Del and L858R were Gabor’s MTRVariance, Gabor’s PTREntropy, and sphericity. Additionally, the respective areas under the receiver operating characteristic curves of the EGFR 19Del and L858R joint models were 0.7925 and 0.7750 for the test set. Conclusions Our study demonstrated the potential for radiomics to predict EGFR 19Del and L858R. Epidermal growth factor receptor 19Del and L858R exhibited distinct imaging phenotypes, which may help to guide the selection of more accurate and personalized treatment programs for patients with NSCLC.