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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
Source PublicationEBIOMEDICINE
ISSN2352-3964
2020-06-01
Volume56Pages:9
Corresponding AuthorZheng, Rongqin(zhengrq@mail.sysu.edu.cn) ; Yu, Jie(jiemi301@163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Ping(liangping301@hotmail.com)
AbstractBackground: 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.
KeywordUltrasound Convolutional neural network Focal liver lesions Diagnosis
DOI10.1016/j.ebiom.2020.102777
Indexed BySCI
Language英语
Funding ProjectNational 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 OrganizationNational 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 AreaGeneral & Internal Medicine ; Research & Experimental Medicine
WOS SubjectMedicine, General & Internal ; Medicine, Research & Experimental
WOS IDWOS:000549929200011
PublisherELSEVIER
Citation statistics
Cited Times:54[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40162
Collection中国科学院分子影像重点实验室
Corresponding AuthorZheng, Rongqin; Yu, Jie; Tian, Jie; Liang, Ping
Affiliation1.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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