Abstract #1811

Diagnosing Metabolic-Associated Fatty Liver Disease in Experimental Rats Using Various Diffusion-Weighted MRI Models

Yutong Zhang¹, Shuangshuang Xie², Xinzhe Du³, Xuyang Wang^4,5, Jiaming Qin³, Jiaqi Yang¹, Jinxia Zhu⁶, Omar Darwish⁷, and Wen Shen⁸

¹First Central Clinical College, Tianjin Medical University, Tianjin, China, ²Department of Radiology, Tianjin First Central Hospital, Tianjin, China, ³The School of Medicine, Nankai University, Tianjin, China, ⁴Medical College of Nankai University, Tianjin, China, ⁵Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China, ⁶MR Research Collaboration, Siemens Healthineers, Beijing, China, ⁷MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany, ⁸Radiology Department, Tianjin First Central Hospital, Tianjin Institute of imaging, School of Medicine, Nankai University, Tianjin, China

Synopsis

Keywords: Liver, Diagnosis/Prediction, MAFLD/MASLD/NAFLD, MASH/NASH, Diffusion magnetic resonance imaging, Diagnostic imaging

Motivation: The increasing prevalence of metabolic-associated steatohepatitis (MASH) requires improved diagnostic tools to noninvasively detect disease severity and progression.

Goal(s): To evaluate the feasibility and performance of six diffusion-weighted imaging (DWI) models in distinguishing MASH from other liver conditions in experimental rats.

Approach: Using multi-b-value diffusion-weighted MRI, diffusion parameters were explored across control, metabolic dysfunction-associated fatty liver disease (MAFLD), and MASH groups. Correlations with MAFLD/NAFLD activity scores and diagnostic accuracy were assessed via receiver operating characteristic curve analysis.

Results: Key diffusion parameters showed significant differences among groups and strong correlations with disease severity scores, outperforming conventional DWI in distinguishing MASH.

Impact: This research highlights the enhanced diagnostic precision of diffusion-weighted MRI for MASH, exceeding the precision of conventional methods. These findings provide a basis for studies regarding early intervention strategies and may lead to refined diagnostic protocols for liver disease severity.

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