Clinical utility of magnetic resonance imaging biomarkers for identifying nonalcoholic steatohepatitis patients at high risk of progression: a multicenter pooled data and meta-analysis
Open accessAbstract
Background & AimsNonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH).MethodsData from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC).ResultsA stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74–0.82), for liver fat was 0.78 (95% CI, 0.73–0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78–0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74–0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64–0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75–0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers.ConclusionsThis study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways. Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH). Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC). A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74–0.82), for liver fat was 0.78 (95% CI, 0.73–0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78–0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74–0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64–0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75–0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers. This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways.
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Corrected T1 values provide a good estimate of the relative proportion of extracellular space and thus inflammatory activity and degree of fibrosis.
“Resulting values provide a good estimate of the relative proportion of extracellular space and thus in fl ammatory activity and degree of fi brosis.”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [160]
The Results and ‘Stratifying high-risk NASH patients with cT1’ sections report that cT1 correlates with lobular inflammation and fibrosis stage. The passages do not say that cT1 estimates the relative proportion of extracellular space, so that proposed explanation is unsupported by our copy. Page numbers are unavailable. (LLM, conf=0.83)
Experimental — accuracy not yet measuredLow availability and high costs limit wider use of LiverMultiScan.
“However, low availability and high costs limit its wider use.”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [160]
The Design and Study Participants section says Perspectum is the only vendor of LiverMultiScan, but does not say the technology has low availability. The discussion describes economic models finding cT1-based pathways cost-effective; it does not say high costs limit wider use. Searches for availability, costs, access, and implementation found no passage establishing the claimed barriers. (LLM, conf=0.75)
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- Authors
- Andersson, A., Kelly, M., Imajo, K., Nakajima, A., Fallowfield, J. A., Hirschfield, G., Pavlides, M., Sanyal, A. J., Noureddin, M., Banerjee, R., Dennis, A., & Harrison, S.
- Affiliations
- Perspectum Ltd. (United Kingdom) (GB)
- Centre for Inflammation Research (GB)
- University Health Network (CA)
- John Radcliffe Hospital (GB)
- Virginia Commonwealth University (US)
- Cedars-Sinai Medical Center (US)
- Corresponding author
- Dennis, Andrea
- ORCID
- Venue
- Clinical Gastroenterology and Hepatology
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 20
- Issue
- 11
- Pages
- 2451-2461.e3
- ISSN
- 1542-3565 (ISSN-L)
- OpenAlex
- W3203473458
- Cited by
- 148
- References
- 60
- Citations by year
- 2026: 26 · 2025: 43 · 2024: 44 · 2023: 26 · 2022: 9
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Gastroenterology and Hepatology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Hepatology › Liver Diseases and Immunity
- Keywords
- nonalcoholic steatohepatitis, non-alcoholic fatty liver disease, liver fibrosis, disease progression, diagnostic accuracy, non-invasive biomarkers, liver cirrhosis, hepatocellular carcinoma, liver transplantation, individual patient data meta-analysis
- MeSH terms
- Humans, Liver, Liver Cirrhosis, Magnetic Resonance Imaging, Multicenter Studies as Topic, Biomarkers, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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