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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

文献类型：期刊论文

作者Wang, Shuo1,2; Shi, Jingyun3; Ye, Zhaoxiang4; Dong, Di1,2; Yu, Dongdong1,2; Zhou, Mu5; Liu, Ying4; Gevaert, Olivier5; Wang, Kun1; Zhu, Yongbei1
刊名EUROPEAN RESPIRATORY JOURNAL
出版日期2019-03-01
卷号53期号:3页码:11
ISSN号0903-1936
DOI10.1183/13993003.00986-2018
通讯作者Tian, Jie(jie.tian@ia.ac.cn)
英文摘要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.
WOS关键词CANCER ; RADIOGENOMICS ; RADIOMICS ; FEATURES ; CLASSIFICATION ; CHEMOTHERAPY ; PHENOTYPES ; DISEASES ; SYSTEM
资助项目National 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]
WOS研究方向Respiratory System
语种英语
WOS记录号WOS:000467523800002
出版者EUROPEAN RESPIRATORY SOC JOURNALS LTD
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/23571]  
专题自动化研究所_学术期刊
自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie
作者单位1.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
推荐引用方式
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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