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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
ISSN0903-1936
2019-03-01
Volume53Issue:3Pages:11
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn)
AbstractEpidermal 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.
DOI10.1183/13993003.00986-2018
WOS KeywordCANCER ; RADIOGENOMICS ; RADIOMICS ; FEATURES ; CLASSIFICATION ; CHEMOTHERAPY ; PHENOTYPES ; DISEASES ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Programme of China[2017YFA0205200] ; National Key R&D Programme of China[2017YFC1308700] ; National Key R&D Programme of China[2017YFC1309100] ; National Key R&D Programme of China[2016YFC010380] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Municipal Science and Technology Commission[Z171100000117023] ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175] ; National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health[R01EB020527]
Funding OrganizationNational Key R&D Programme of China ; National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health
WOS Research AreaRespiratory System
WOS SubjectRespiratory System
WOS IDWOS:000467523800002
PublisherEUROPEAN RESPIRATORY SOC JOURNALS LTD
Citation statistics
Cited Times:19[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.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Resp Med, Shanghai, Peoples R China
4.Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc, Dept Radiol,Key Lab Canc Prevent & Therapy, Tianjin, Peoples R China
5.Stanford Univ, Stanford Ctr Biomed Informat Res, Dept Med, Stanford, CA USA
6.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
7.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Databased Precis Med, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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(3):11.
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(3),11.
MLA Wang, Shuo,et al."Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning".EUROPEAN RESPIRATORY JOURNAL 53.3(2019):11.
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