Observational Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. May 26, 2019; 7(10): 1122-1132
Published online May 26, 2019. doi: 10.12998/wjcc.v7.i10.1122
Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis
Zhao-Cheng Jian, Jin-Feng Long, Yu-Jiang Liu, Xiang-Dong Hu, Ji-Bin Liu, Xian-Quan Shi, Wei-Sheng Li, Lin-Xue Qian
Zhao-Cheng Jian, Yu-Jiang Liu, Xiang-Dong Hu, Xian-Quan Shi, Wei-Sheng Li, Lin-Xue Qian, Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
Zhao-Cheng Jian, Jin-Feng Long, Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang 261031, Shandong Province, China
Ji-Bin Liu, Institute of Ultrasound, Thomas Jefferson University Hospital, Philadelphia, PA 19107, United States
Author contributions: All authors helped to perform the research; Jian ZC manuscript writing, performing procedures and data analysis; Long JF contribution to writing the manuscript, performing experiments, and data analysis; Liu YJ and Hu XD contribution to performing experiments; Liu JB contribution to writing the manuscript; Shi XQ and Li WS contribution to data analysis; Qian LX manuscript writing, drafting conception and design, performing experiments, and data analysis.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University.
Informed consent statement: Patient's informed consent was obtained before the study,though the clinical data used in this study were anonymous.
Conflict-of-interest statement: All authors declare no conflicts-of-interest related to this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: Lin-Xue Qian, MD, Chief Doctor, Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing 100050, China. qianlinxue2002@163.com
Telephone: +86-13562645007 Fax: +86-10-63138576
Received: February 2, 2019
Peer-review started: February 2, 2019
First decision: March 10, 2019
Revised: March 19, 2019
Accepted: April 8, 2019
Article in press: April 9, 2019
Published online: May 26, 2019
Processing time: 113 Days and 8.1 Hours
ARTICLE HIGHLIGHTS
Research background

Two dimensional shear wave elastography (2D-SWE) has been widely used in non-invasive diagnosis of liver fibrosis due to its non-invasiveness, reproducibility and high accuracy. However, it is not effective in the diagnosis of early liver fibrosis and it is not completely replaceable with liver biopsy; therefore, how to further improve the diagnostic efficacy of 2D-SWE examination on liver fibrosis staging is a clinically urgent problem. Texture analysis has always been a hotspot of image analysis, and texture analysis of medical images has also been achieved good results in the diagnosis and treatment of many diseases. But there are few studies on texture analysis for ultrasound elastic images currently. The combination of the two is expected to improve the diagnostic efficacy of liver fibrosis, especially early liver fibrosis in patients with chronic hepatitis B.

Research motivation

In order to improve the efficacy of non-invasive diagnosis of early liver fibrosis in patients with chronic hepatitis B, the information of elastic images obtained by 2D-SWE examination should be fully applied. This study intends to use the texture analysis software to deeply analyze the elastic images and obtain the spatial distribution information of elastic modulus, meanwhile, combined with the Young's modulus value to diagnose the liver fibrosis. It will provide a new idea and method for the diagnosis of liver fibrosis in patients with chronic hepatitis B.

Research objectives

The objective of this study was to find a non-invasive, reproducible, and accurate method for the diagnosis of early liver fibrosis caused by chronic hepatitis B. The study demonstrated that combination of 2D-SWE with texture analysis can effectively improve the diagnostic efficacy of early liver fibrosis, which provides theoretical support for the application of texture analysis in the diagnosis of early liver fibrosis in patients with chronic hepatitis B, and it also provides a possibility for non-invasive diagnosis to gradually replace the liver tissue biopsy.

Research methods

Based on the 2D-SWE examination, this study applied texture analysis software (SSI) to obtain the mean values at different angles of four texture patterns (contrast, correlation, ASM and homogeneity). Take pathological results of biopsy specimens as the gold standards to orderly test: comparison and assessment of the diagnosis efficiency conducted for 2D-SWE, contrast, correlation, ASM, homogeneity and their combination. The feasibility of 2D-SWE combined texture analysis in the diagnosis of liver fibrosis was discussed by analyzing the spatial distribution characteristics of elastic modulus, and combined with the application of Young's modulus value.

Research results

The study demonstrated that contrast and homogeneity have separated diagnostic efficacy in the diagnosis of liver fibrosis in patients with chronic hepatitis B. The AUC values of each group in the combined diagnosis are improved compared with the separated diagnosis of each index. The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis. This study is the first to apply the texture analysis of elastic images to the non-invasive diagnosis of liver fibrosis, and confirmed the value of contrast and homogeneity in the diagnosis of liver fibrosis, and found that combined diagnosis can improve the diagnostic efficacy of early liver fibrosis. In the further study, it is necessary to continue to explore the influence of different angles on the diagnostic performance of texture features and the feasibility of combined application with other liver fibrosis diagnostic prediction models.

Research conclusions

The main conclusions of this study are as follows: (1) Texture analysis of elastic images can be applied in the diagnosis of liver fibrosis with chronic hepatitis B, in which the diagnostic efficacy of contrast and homogeneity is comparable to 2D-SWE, but correlation and ASM showed poor diagnosis efficiency; (2) Combined diagnosis (2D-SWE plus texture analysis) can effectively improve the diagnostic efficacy of liver fibrosis in patients with chronic hepatitis B, especially in the diagnosis of early liver fibrosis, combined diagnostic efficacy is better; and (3) The staging of liver fibrosis in chronic hepatitis B may be related to the spatial heterogeneity of liver tissue hardness distribution. The more spatial heterogeneity of hardness distribution, the more severe the degree of liver fibrosis.

Research perspectives

This study confirmed that the post-processing of elastic images can further improve the diagnostic value of ultrasound-elastic images for liver fibrosis. However, the sample size of this study is small, and other diagnostic indicators related to liver fibrosis have not been combined. A robust diagnosis model of liver fibrosis has not yet been established. In the follow-up study, while optimizing the texture analysis, the sample size will be expanded, and other liver fibrosis diagnostic methods may be combined to establish a non-invasive diagnostic model for early liver fibrosis with higher efficacy.