Prospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 7, 2024; 30(9): 1164-1176
Published online Mar 7, 2024. doi: 10.3748/wjg.v30.i9.1164
Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models
Yan-Li Jiang, Juan Li, Peng-Fei Zhang, Feng-Xian Fan, Jie Zou, Pin Yang, Peng-Fei Wang, Shao-Yu Wang, Jing Zhang
Yan-Li Jiang, Peng-Fei Zhang, Feng-Xian Fan, Jie Zou, Pin Yang, Peng-Fei Wang, Jing Zhang, Department of Magnetic Resonance Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, Gansu Province, China
Yan-Li Jiang, Peng-Fei Zhang, Second Clinical School, Lanzhou University, Lanzhou 730030, Gansu Province, China
Yan-Li Jiang, Feng-Xian Fan, Jie Zou, Pin Yang, Peng-Fei Wang, Jing Zhang, Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, Gansu Province, China
Juan Li, Department of Hepatology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, Gansu Province, China
Shao-Yu Wang, MR Scientific Marketing, Siemens Healthineers, Xi’an 710065, Shaanxi Province, China
Co-first authors: Yan-Li Jiang and Juan Li.
Author contributions: Jiang YL, Li J, Wang SY, and Zhang J conceived, designed and refined the study protocol; Zou and Wang PF were involved in the data collection; Zhang PF, Yang P, and Wang SY analyzed the data; Jiang YL, Li J, and Fan FX drafted the manuscript; All authors were involved in the critical review of the results and have contributed to, read, and approved the final manuscript. Jiang YL and Li J contributed equally to this work as co-first author. The reasons for designating Jiang YL and Li J as co-first authors are threefold. First, the research was performed as a collaborative effort, and the designation of co-first authorship accurately reflects the distribution of responsibilities and burdens associated with the time and effort required to complete the study and the resultant paper. Second, the overall research team encompassed authors with a variety of expertise and skills from different fields, and the designation of co-first authors best reflects this diversity. This also promotes the most comprehensive and in-depth examination of the research topic, ultimately enriching readers’ understanding by offering various expert perspectives. Third, Jiang YL and Li J contributed efforts of equal substance throughout the research process. The choice of these researchers as co-first authors acknowledges and respects this equal contribution, while recognizing the spirit of teamwork and collaboration of this study. In summary, we believe that designating Jiang YL and Li J as co-first authors is fitting for our manuscript as it accurately reflects our team’s collaborative spirit, equal contributions, and diversity.
Supported by the Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital, NO. CY2021-QN-B09; the Science and Technology Project of Gansu Province, NO. 21JR11RA122; Department of Education of Gansu Province: Innovation Fund Project, NO. 2022B-056; and Gansu Province Clinical Research Center for Functional and Molecular Imaging, NO. 21JR7RA438.
Institutional review board statement: The ethics committee of Lanzhou University Second Hospital approved this prospective study (2021A-423).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors of this manuscript having no conflicts of interest to disclose.
Data sharing statement: Availability of data and materials all data generated and analyzed during the current study will be available from the corresponding author on reasonable request.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jing Zhang, Doctor, MD, PhD, Researcher, Department of Magnetic Resonance Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou 730030, Gansu Province, China. ery_zhangjing@lzu.edu.cn
Received: October 22, 2023
Peer-review started: October 22, 2023
First decision: January 5, 2024
Revised: January 15, 2024
Accepted: February 7, 2024
Article in press: February 7, 2024
Published online: March 7, 2024
Processing time: 135 Days and 14.9 Hours
ARTICLE HIGHLIGHTS
Research background

Liver fibrosis is a public health problem and closely associated with various prevalent causes of chronic liver damage. Early diagnosis and accurate staging of liver fibrosis are important in clinical practice. Non-invasive methods have been evaluated for diagnosing and staging liver fibrosis and have become the focus of clinical research. Diffusion-weighted imaging (DWI) represents the most widely used functional magnetic resonance imaging (MRI) sequence. Several DWI models are used in clinical practice. The quantitative information gathered from some of these models was used to detect and stage liver fibrosis.

Research motivation

Early liver fibrosis detection and staging are based on conventional DWI or early non-Gaussian diffusion models. The liver fibrosis staging performance and the ability to distinguish significant fibrosis (SF) of some novel DWI models were not fully clear.

Research objectives

In this prospective study, we investigated the value of the newest diffusion models in staging liver fibrosis and compare their performances in distinguishing SF.

Research methods

This study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants without serious health problems from July 2021 to June 2022. All participants underwent multi-b value DWI and then calculated to various DWI models using an in-house software prototype developed by MR Station. The main DWI-derived parameters included Mono-apparent diffusion coefficient (ADC) from mono-exponential DWI, intravoxel incoherent motion model-derived true diffusion coefficient (IVIM-D), diffusion kurtosis imaging-derived apparent diffusivity, stretched exponential model-derived distributed diffusion coefficient (SEM-DDC), fractional order calculus (FROC) model-derived diffusion coefficient (FROC-D) and FROC model-derived microstructural quantity (FROC-μ), continuous-time random-walk (CTRW) model-derived anomalous diffusion coefficient (CTRW-D) and CTRW model-derived temporal diffusion heterogeneity index (CTRW-α). The correlations between DWI-derived parameters and fibrosis stages and the parameters’ diagnostic efficacy in detecting SF were assessed and compared.

Research results

In the current study, it was found that liver fibrosis stages differed significantly in Mono-ADC, IVIM-D, FROC-D, and CTRW-D. The fibrosis stages showed significant inverse correlations with Mono-ADC, IVIM-D, DKI-derived apparent diffusivity, SEM-DDC, FROC-D, FROC-μ, CTRW-D, and CTRW-α. The combined CTRW-derived parameters resulted in the highest areas under the ROC curve (0.747).

Research conclusions

The CTRW-DWI model demonstrated the clinical potential in liver fibrosis staging. The combined diffusion parameters based on the various models were superior to each individual parameter in distinguishing SF.

Research perspectives

As advanced DWI models, FROC and CTRW demonstrated their clinical potential in early detection of liver fibrosis. More patients and stratification of causes will help to generate more accurate results. Also, normalization of the DWI parameters will improve the effectiveness and power in future research.