Knowledge Commons of Institute of Automation，CAS
| 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
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| 发表期刊 | EUROPEAN RESPIRATORY JOURNAL
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| ISSN | 0903-1936 |
| 2019-03-01 | |
| 卷号 | 53期号:3页码:11 |
| 通讯作者 | Tian, Jie(jie.tian@ia.ac.cn) |
| 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. |
| DOI | 10.1183/13993003.00986-2018 |
| 关键词[WOS] | CANCER ; RADIOGENOMICS ; RADIOMICS ; FEATURES ; CLASSIFICATION ; CHEMOTHERAPY ; PHENOTYPES ; DISEASES ; SYSTEM |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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] ; 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] |
| 项目资助者 | 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 |
| WOS研究方向 | Respiratory System |
| WOS类目 | Respiratory System |
| WOS记录号 | WOS:000467523800002 |
| 出版者 | EUROPEAN RESPIRATORY SOC JOURNALS LTD |
| 七大方向——子方向分类 | 医学影像处理与分析 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/23571 |
| 专题 | 学术期刊 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 |
| 第一作者单位 | 中国科学院分子影像重点实验室 |
| 通讯作者单位 | 中国科学院分子影像重点实验室 |
| 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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