Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study | |
Yang, Qi1; Wei, Jingwei2,3 ; Hao, Xiaohan2,3,4; Kong, Dexing5; Yu, Xiaoling1; Jiang, Tianan6; Xi, Junqing1; Cai, Wenjia1; Luo, Yanchun1; Jing, Xiang7; Yang, Yilin8; Cheng, Zhigang1; Wu, Jinyu9; Zhang, Huiping10; Liao, Jintang11; Zhou, Pei12; Song, Yu13; Zhang, Yao14; Han, Zhiyu1; Cheng, Wen; Tang, Lina15,16; Liu, Fangyi1; Dou, Jianping1; Zheng, Rongqin17,18; Yu, Jie1; Tian, Jie2,3,19; Liang, Ping1
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| Source Publication | EBIOMEDICINE
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| ISSN | 2352-3964 |
| 2020-06-01 | |
| Volume | 56Pages:9 |
| Corresponding Author | Zheng, Rongqin(zhengrq@mail.sysu.edu.cn) ; Yu, Jie(jiemi301@163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Ping(liangping301@hotmail.com) |
| Abstract | Background: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. Materials and methods: This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. Findings: The AUC of Model(LBC) for FLLs was 0.924 (95% CI: 0.889-0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. Interpretation: DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis. (C) 2020 The Authors. Published by Elsevier B.V. |
| Keyword | Ultrasound Convolutional neural network Focal liver lesions Diagnosis |
| DOI | 10.1016/j.ebiom.2020.102777 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Scientific Foundation Committee of China[81627803] ; National Scientific Foundation Committee of China[81971625] ; National Scientific Foundation Committee of China[91859201] ; National Scientific Foundation Committee of China[81227901] ; National Scientific Foundation Committee of China[81527805] ; National Scientific Foundation Committee of Beijing[JQ18021] ; Fostering Funds for National Distinguished Young Scholar Science Fund[NCRCG-PLAGH-2019011] ; National Clinical Research Centre for Geriatric Diseases of Chinese PLA General Hospital ; National Key R&D Program of Ministry of Science and Technology of China[2018ZX10723-204] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Strategic Priority Research Program of Chinese Academy of Science[XDBS01000000] |
| Funding Organization | National Scientific Foundation Committee of China ; National Scientific Foundation Committee of Beijing ; Fostering Funds for National Distinguished Young Scholar Science Fund ; National Clinical Research Centre for Geriatric Diseases of Chinese PLA General Hospital ; National Key R&D Program of Ministry of Science and Technology of China ; Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission ; Strategic Priority Research Program of Chinese Academy of Science |
| WOS Research Area | General & Internal Medicine ; Research & Experimental Medicine |
| WOS Subject | Medicine, General & Internal ; Medicine, Research & Experimental |
| WOS ID | WOS:000549929200011 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/40162 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Zheng, Rongqin; Yu, Jie; Tian, Jie; Liang, Ping |
| Affiliation | 1.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, 28 Fuxing Rd, Beijing 100853, Peoples R China 2.Chinese Acad Sci, Key Lab Mol Imaging, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Univ Sci & Technol China, Ctr Biomed Engn, Hefei, Peoples R China 5.Zhejiang Univ, Sch Math Sci, Hangzhou, Peoples R China 6.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Ultrasound, Hangzhou, Jiangsu, Peoples R China 7.Tianjin Third Cent Hosp, Dept Ultrasound, Tianjin, Peoples R China 8.Fourth Mil Med Univ, Tangdu Hosp, Dept Ultrasound Diag, Xian, Peoples R China 9.Harbin First Hosp, Dept Ultrasound, Harbin, Peoples R China 10.Maanshan Peoples Hosp, Dept Med Ultrasound, Maanshan, Peoples R China 11.Xiangya Hosp, Dept Diagnost Ultrasound, Changsha, Peoples R China 12.Chinese Peoples Liberat Army, Cent Theater Command Gen Hosp, Dept Ultrasound, Wuhan, Peoples R China 13.Dalian Med Univ, Affiliated Hosp 2, Dept Diagnost Ultrasound, Dalian, Peoples R China 14.Capital Med Univ, Beijing Ditan Hosp, Dept Ultrasound, Beijing, Peoples R China 15.Harbin Med Univ, Canc Hosp, Dept Ultrasound, Harbin, Peoples R China 16.Fujian Canc Hosp, Dept Ultrasound, Fuzhou, Peoples R China 17.Fujian Med Univ, Canc Hosp, Fuzhou, Peoples R China 18.Sun Yat Sen Univ, Affiliated Hosp 3, Guangdong Key Lab Liver Dis Res, Dept Med Ultrasound, Guangzhou, Peoples R China 19.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China |
| Recommended Citation GB/T 7714 | Yang, Qi,Wei, Jingwei,Hao, Xiaohan,et al. Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study[J]. EBIOMEDICINE,2020,56:9. |
| APA | Yang, Qi.,Wei, Jingwei.,Hao, Xiaohan.,Kong, Dexing.,Yu, Xiaoling.,...&Liang, Ping.(2020).Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.EBIOMEDICINE,56,9. |
| MLA | Yang, Qi,et al."Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study".EBIOMEDICINE 56(2020):9. |
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