Meta-Analysis
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 26, 2020; 8(22): 5589-5602
Published online Nov 26, 2020. doi: 10.12998/wjcc.v8.i22.5589
Interobserver agreement for contrast-enhanced ultrasound of liver imaging reporting and data system: A systematic review and meta-analysis
Jun Li, Ming Chen, Zi-Jing Wang, Shu-Gang Li, Meng Jiang, Long Shi, Chun-Li Cao, Tian Sang, Xin-Wu Cui, Christoph F Dietrich
Jun Li, Ming Chen, Zi-Jing Wang, Chun-Li Cao, Tian Sang, Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi 832008, Xinjiang Uygur Autonomous Region, China
Shu-Gang Li, Department of Child, Adolescent Health and Maternal Health, School of Public Health, Capital Medical University, Beijing 100069, Beijing, China
Meng Jiang, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Long Shi, Department of Medical Ultrasound, The Second People's Hospital of Jiangmen, Jingmen 448000, Hubei Province, China
Xin-Wu Cui, Department of Medical Ultrasound, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Christoph F Dietrich, Department of Internal Medicine, Hirslanden Clinic, Berne 27804, Switzerland
Author contributions: Li J and Chen M contributed equally to this article; Li J, Cui XW, Shi L, and Dietrich CF designed the research; Li J and Chen M performed the research; Jiang M and Sang T contributed to analytic tools; Cao CL and Li SG analyzed data; Chen M and Wang ZJ wrote the paper; Shi L, Cui XW, and Dietrich CF revised the article and approved the final version.
Supported by Health Commission of Hubei Province, China No. WJ2019M077 and No. WJ2019H227; Natural Science Foundation of Hubei Province, China No. 2019CFB286; and Science and Technology Bureau of Shihezi, China No. 2019ZH11.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other co-authors who contributed their efforts in this manuscript.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xin-Wu Cui, MD, PhD, Professor, Department of Medical Ultrasound, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, Hubei Province, China. cuixinwu@live.cn
Received: April 29, 2020
Peer-review started: April 29, 2020
First decision: July 29, 2020
Revised: August 11, 2020
Accepted: September 29, 2020
Article in press: September 29, 2020
Published online: November 26, 2020
Processing time: 210 Days and 0.5 Hours
Core Tip

Core Tip: From the results of previous studies, Liver Imaging Reporting and Data System (LI-RADS) on contrast-enhanced ultrasound (CEUS) has shown a satisfactory diagnostic value. However, a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined. In this article, we included 8 relevant articles to exploring interobserver agreement of LI-RADS on CEUS by making a meta-analysis. Lastly, meta-analysis results revealed that the summary Kappa value of included studies showed substantial agreement, and meta-regression identified the variables, including the method of patient enrolment, method of consistency testing, and patient race, which explained the substantial study heterogeneity.