Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study

doi:10.1016/j.ebiom.2020.102777

	Research paper Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study
	Yang, Qi 1; Wei, Jingwei2,3 ; Hao, Xiaohan 2,3,4; Kong, Dexing 5; Yu, Xiaoling 1; Jiang, Tianan 6; Xi, Junqing 1; Cai, Wenjia 1; Luo, Yanchun 1; Jing, Xiang 7; Yang, Yilin 8; Cheng, Zhigang 1; Wu, Jinyu 9; Zhang, Huiping 10; Liao, Jintang 11; Zhou, Pei 12; Song, Yu 13; Zhang, Yao 14; Han, Zhiyu 1; Cheng, Wen ; Tang, Lina 15,16; Liu, Fangyi 1; Dou, Jianping 1; Zheng, Rongqin 17,18; Yu, Jie 1; Tian, Jie 2,3,19; Liang, Ping 1
发表期刊	EBIOMEDICINE
ISSN	2352-3964
	2020-06-01
卷号	56 页码:9
通讯作者	Zheng, Rongqin(zhengrq@mail.sysu.edu.cn) ; Yu, Jie(jiemi301@163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Ping(liangping301@hotmail.com)
摘要	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.
关键词	Ultrasound Convolutional neural network Focal liver lesions Diagnosis
DOI	10.1016/j.ebiom.2020.102777
收录类别	SCI
语种	英语
资助项目	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]
项目资助者	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研究方向	General & Internal Medicine ; Research & Experimental Medicine
WOS类目	Medicine, General & Internal ; Medicine, Research & Experimental
WOS记录号	WOS:000549929200011
出版者	ELSEVIER
引用统计	被引频次：49[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/40162
专题	中国科学院分子影像重点实验室
通讯作者	Zheng, Rongqin; Yu, Jie; Tian, Jie; Liang, Ping
作者单位	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
推荐引用方式 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.