Evidence map›Paper›PMID 41494787›Full record

ReviewKorean journal of radiology2026

Imaging Evaluation for Steatotic Liver Disease.

Shin Mei Chan, Vitor F Martins, Kathleen Marsh, Kang Wang, Jake T Weeks, Aiguo Han, Meng Yin, Kathryn J Fowler, Claude B Sirlin, Cheng William Hong

Abstract readReview
In one paragraph

Review in Korean journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Shin Mei ChanDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0001-9318-0335
Vitor F MartinsLiver Imaging Group, University of California San Diego, San Diego, CA, USA.ORCID https://orcid.org/0000-0002-9060-1314
Kathleen MarshDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0003-0287-7263
Kang WangDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0002-6666-5641
Jake T WeeksLiver Imaging Group, University of California San Diego, San Diego, CA, USA.ORCID https://orcid.org/0009-0001-2645-9034
Aiguo HanDepartment of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.ORCID https://orcid.org/0000-0001-7606-9221
Meng YinDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0001-6778-192X
Kathryn J FowlerScripps Clinic Medical Group, La Jolla, CA, USA.ORCID https://orcid.org/0000-0001-9188-1701
Claude B SirlinLiver Imaging Group, University of California San Diego, San Diego, CA, USA.ORCID https://orcid.org/0000-0002-6639-9072
Cheng William HongDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. cheng.hong@ucsf.edu.ORCID https://orcid.org/0000-0002-5219-6039

Funding

Traning Clinical Scientists in Radiological ImagingT32EB005970 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Eric Y Chang, Rebecca Ann Rakow-Penner · 2007 to 2026
$3.2M
NIBIB NIH HHS T32 EB005970
6 · The paper itself

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as nonalcoholic fatty liver disease, is the fastest-growing cause of chronic liver disease worldwide, affecting approximately 30% of the global population. Imaging is vital for detecting, quantifying, and monitoring hepatic steatosis-the defining abnormality of MASLD-and subsequent fibrosis-the key determinant of liver-related outcomes. This review summarizes the principles, clinical usage, efficacy, and advancements in various imaging modalities for the noninvasive assessment of hepatic steatosis and fibrosis, with an emphasis on ultrasound, CT, and MRI. Additionally, this review explores the evolving landscape of MASLD diagnostic approaches, including machine-learning techniques, opportunistic screening, standardized imaging guidelines, and therapies, emphasizing the pivotal role that radiologists can play in shaping these developments.

Indexed as

Diagnostic ImagingNon-alcoholic Fatty Liver DiseaseHumansLiverLiver CirrhosisMagnetic Resonance ImagingTomography, X-Ray ComputedUltrasonographyChemical-shift-encodedCSE-MRIMASLDMetabolic dysfunction-associated steatotic liver diseaseMR elastographyPDFFProton density fat fractionQuantitative ultrasound

Identifiers

PMID41494787
PMCPMC12865115

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.