SynthesisJournal of gastroenterology and hepatology2025
Liver Fat Quantification and Steatosis Grading in Fatty Liver Disease by Magnetic Resonance Imaging: Systematic Review and Meta-Analysis.
Synthesis in Journal of gastroenterology and hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Liver Fat Quantification and Steatosis Grading in Fatty Liver Disease by Magnetic Resonance Imaging: Systematic Review and Meta-Analysis.Journal of gastroenterology and hepatology · 2025Pooled it
- Comparison of Manual Multi-ROI, Single-Location, and Volumetric Fat Fraction Measurements for Hepatic Steatosis Using MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic performance of liver steatosis analysis and ultrasound-guided attenuation parameter in quantifying hepatic steatosis: a comparative evaluation using controlled attenuation parameter as reference.Frontiers in physiology · 2026Article
- Correlation analysis and diagnostic performance of CT/MRI fat quantification parameters in metabolic dysfunction-associated steatotic liver disease.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
aimMagnetic resonance imaging (MRI) surrogate biomarkers are widely utilized to diagnose and monitor metabolic dysfunction-associated steatotic liver disease (MASLD). This meta-analysis assesses their effectiveness in quantifying liver fat and grading steatosis. METHODS AND
resultsScopus, Web of Science, and MEDLINE (PubMed) were systematically searched up to February 20, 2024. Eligible studies included original research in English, focusing on MRI-based assessments of hepatic steatosis in humans with no comorbidities, with magnetic resonance spectroscopy (MRS) or pathology as the reference standard. Fat fraction and steatosis grade were evaluated using correlation, mean difference, and diagnostic accuracy metrics. This review was registered with PROSPERO with registration number CRD42024524991. Out of 9852 unique studies, 18 were included. Selective saturation fat fraction demonstrated the strongest Fisher's Z-transformed correlation with the reference standard (0.93), followed by proton density fat fraction (PDFF) (0.9). PDFF exhibited the highest Fisher's Z-transformed correlation with hepatic steatosis grading of the reference standard (0.67) and the highest AUROC for diagnosing hepatic steatosis (0.99). Magnetic resonance elastography (MRE) and MRI-PDFF multiparametric MRI showed the greatest sensitivity (0.97) for hepatic steatosis diagnosis, while multiecho gradient echo (MEGE) demonstrated the highest specificity (0.99).
conclusionsThe analysis revealed moderate to strong correlations between MRI and reference standard FF, with MRI-PDFF demonstrating the highest diagnostic accuracy. There was significant variability in results across studies due to differences in techniques, field strength, and study design. The findings underscore the need for standardized performance metrics and reporting practices to enhance comparability, optimize clinical utility, and improve diagnostic uniformity for hepatic steatosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.