Comparing and combining MRE, T1ρ, SWI, IVIM, and DCE-MRI for the staging of liver fibrosis in rabbits: Assessment of a predictive model based on multiparametric MRI

Liqiu Zou; Jinzhao Jiang; Hao Zhang; Wenxin Zhong; Min Xiao; Shunbao Xin; Yang Wang; Wei Xing

doi:10.1002/mrm.29126

Comparing and combining MRE, T1ρ, SWI, IVIM, and DCE-MRI for the staging of liver fibrosis in rabbits: Assessment of a predictive model based on multiparametric MRI

Magn Reson Med. 2022 May;87(5):2424-2435. doi: 10.1002/mrm.29126. Epub 2021 Dec 21.

Authors

Liqiu Zou¹, Jinzhao Jiang², Hao Zhang¹, Wenxin Zhong¹, Min Xiao¹, Shunbao Xin¹, Yang Wang¹, Wei Xing³

Affiliations

¹ Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, China.
² Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, China.
³ Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, China.

PMID: 34931716
DOI: 10.1002/mrm.29126

Abstract

Purpose: To establish and validate an optimal predictive model based on multiparametric MRI for staging liver fibrosis (LF) in rabbits with magnetic resonance elastography (MRE), spin-lattice relaxation time in the rotating frame (T1ρ imaging), SWI, intravoxel incoherent motion (IVIM), and DCE-MRI.

Methods: The LF group included 120 rabbits induced by subcutaneous injections of carbon tetrachloride (CCl₄ ); 30 normal rabbits served as the control group. Multiparametric MRI was performed, including MRE, T1ρ, SWI, IVIM, and DCE-MRI. The quantitative parameters were analyzed in two groups, with histopathological results serving as the reference standard. The diagnostic performance of multiparametric MRI and the predictive model established by multivariable logistic regression analysis were evaluated by receiver operating characteristic (ROC) curve analysis.

Results: In total, 32, 67, and 51 rabbits were histologically diagnosed as no fibrosis (stage F0), early-stage LF (F1-F2), and advanced-stage LF (F3-F4), respectively. The LF stages presented a strong correlation with liver stiffness (LS) on MRE (r = 0.90), signal-intensity ratio (SIR) on SWI (r = -0.84), and K^trans on DCE-MRI (r = 0.71; p < 0.05 for all). The LS and SIR parameters had higher AUC values for distinguishing early-stage LF from both no fibrosis (0.94 and 0.93, respectively) and advanced-stage LF (0.95 and 0.87, respectively). The predictive model showed a slightly higher AUC value of 0.97 (0.90-0.99) than LS and SIR in distinguishing early-stage LF from no fibrosis (p > 0.05), a significantly higher AUC value of 0.98 (0.93-0.99) than the SIR in distinguishing early-stage from advanced-stage LF (p < 0.05).

Conclusion: SWI, DCE-MRI, and MRE in particular showed improved performance for LF diagnosis and stage. The predictive model based on multiparametric MRI was found to further enhance diagnostic accuracy and could serve as an excellent imaging tool for staging LF.

Keywords: liver fibrosis; magnetic resonance elastography; multiparametric MRI; predictive model; susceptibility-weighted imaging.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Elasticity Imaging Techniques* / methods
Liver / diagnostic imaging
Liver / pathology
Liver Cirrhosis / diagnostic imaging
Magnetic Resonance Imaging / methods
Motion
Multiparametric Magnetic Resonance Imaging*
Rabbits