Evaluation of liver fibrosis staging in patients with chronic hepatitis via the gadolinium washout rate: a comparative study with magnetic resonance elastography and pathology
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Liver fibrosis, which affects over 200 million patients with chronic hepatitis across the world, is a critical prognostic determinant, and its accurate staging is necessary for guiding interventions. Although biopsy remains the gold standard, its invasiveness and sampling variability necessitate the development of reliable noninvasive alternatives. Magnetic resonance elastography (MRE) offers high diagnostic accuracy but is limited by cost and hardware requirements. Gadolinium-based agents [e.g., gadobenate dimeglumine (Gd-BOPTA)] enable hemodynamic quantification, yet no integrated metric combines washout and wash-in dynamics for fibrosis staging. This study aimed to develop and validate the gadolinium washout rate (GWR)-a novel model integrating multiphase T1 mapping washout (renal clearance) and wash-in (hepatocellular uptake) features-for precise, accessible liver fibrosis staging. Methods: This retrospective study enrolled 174 patients with chronic hepatitis. Liver T1 mapping [unenhanced, 5-minute delayed, and 120-minute hepatobiliary phase (HBP)] and biopsy were performed. GWR, the relative enhancement rate of the HBP and delayed phase in the T1 mode and MRE were compared via Pearson correlation, logistic regression, receiver operating characteristic analysis, and fivefold cross-validation. Results: 0.912). Cross-validation confirmed GWR's robustness (AUC =0.959, accuracy=97.1%, and sensitivity=0.929). Conclusions: GWR is superior to MRE in diagnosing advanced fibrosis (≥ F3), offering higher predictive values and clinical net benefit. It provides a cost-effective, noninvasive alternative to MRE.
Authors: Zhanao Meng, Sidong Xie, Jian Cao, Xue Lin, Tao Luo, Xiaolei Li, Sisi Deng, Yue Zhang, Ke Zhang, Xuan Zhu, Na Cheng, Haifeng Li, Tianhao Tang, Qing Xiang, Yahao Guo, Jie Qin
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414483631