3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma.

Content Provider	Europe PMC
Author	Liu, Guixue Xu, Zhihan Ge, Yingqian Jiang, Beibei Groen, Harry Vliegenthart, Rozemarijn Xie, Xueqian
Copyright Year	2020
Abstract	BackgroundTo establish a radiomic approach to identify epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients based on CT images, and to distinguish exon-19 deletion and exon-21 L858R mutation.MethodsTwo hundred sixty-three patients who underwent pre-surgical contrast-enhanced CT and molecular testing were included, and randomly divided into the training (80%) and test (20%) cohort. Tumor images were three-dimensionally segmented to extract 1,672 radiomic features. Clinical features (age, gender, and smoking history) were added to build classification models together with radiomic features. Subsequently, the top-10 most relevant features were used to establish classifiers. For the classifying tasks including EGFR mutation, exon-19 deletion, and exon-21 L858R mutation, four logistic regression models were established for each task.ResultsThe training and test cohort consisted of 210 and 53 patients, respectively. Among the established models, the highest accuracy and sensitivity among the four models were 75.5% (61.7–86.2%) and 92.9% (76.5–99.1%) to classify EGFR mutation, respectively. The highest specificity values were 86.7% (69.3–96.2%) and 70.4% (49.8–86.3%) to classify exon-19 deletion and exon-21 L858R mutation, respectively.ConclusionsCT radiomics can sensitively identify the presence of EGFR mutation, and increase the certainty of distinguishing exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma patients. CT radiomics may become a helpful non-invasive biomarker to select EGFR mutation patients for invasive sampling.
ISSN	22186751
Journal	Translational Lung Cancer Research
Volume Number	9
PubMed Central reference number	PMC7481623
Issue Number	4
PubMed reference number	32953499
e-ISSN	22264477
DOI	10.21037/tlcr-20-122
Language	English
Publisher	AME Publishing Company
Publisher Date	2020-08-01
Access Restriction	Open
Rights License	Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0. 2020 Translational Lung Cancer Research. All rights reserved.
Subject Keyword	Radiomics epidermal growth factor receptor adenocarcinoma of lung tomography, X-ray computed
Content Type	Text
Resource Type	Article
Subject	Oncology