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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning
Wang, Shuo1,2; Shi, Jingyun3; Ye, Zhaoxiang4; Dong, Di1,2; Yu, Dongdong1,2; Zhou, Mu5; Liu, Ying4; Gevaert, Olivier5; Wang, Kun1; Zhu, Yongbei1; Zhou, Hongyu6; Liu, Zhenyu1; Tian, Jie1,2,7
Source PublicationEuropean Respiratory Journal
2019-01
Issue53Pages:1800986
Abstract

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines
such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR
genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of
accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung
adenocarcinoma using non-invasive computed tomography (CT).
We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images,
EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was
proposed to predict the EGFR mutation status by CT scanning.
By training in 14926 CT images, the deep learning model achieved encouraging predictive performance
in both the primary cohort (n=603; AUC 0.85, 95% CI 0.83–0.88) and the independent validation cohort
(n=241; AUC 0.81, 95% CI 0.79–0.83), which showed significant improvement over previous studies using
hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated
significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).
Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive
and easy-to-use method for EGFR mutation status prediction.

KeywordLung Adenocarcinoma Epidermal Growth Factor Receptor Mutation Lung Cancer Deep Learning Artificial Intelligence Health Informatics
DOI10.1183/13993003.00986-2018
Indexed BySCI
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23571
Collection学术期刊
中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
2.University of Chinese Academy of Sciences, Beijing, China.
3.Dept of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
4.Dept of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
5.The Stanford Center for Biomedical Informatics Research, Dept of Medicine, Stanford University, Stanford, CA, USA.
6.Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
7.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Shuo,Shi, Jingyun,Ye, Zhaoxiang,et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning[J]. European Respiratory Journal,2019(53):1800986.
APA Wang, Shuo.,Shi, Jingyun.,Ye, Zhaoxiang.,Dong, Di.,Yu, Dongdong.,...&Tian, Jie.(2019).Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.European Respiratory Journal(53),1800986.
MLA Wang, Shuo,et al."Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning".European Respiratory Journal .53(2019):1800986.
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