An MRI-based technique appears to be an effective, noninvasive option for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) patients at risk of metabolic dysfunction-associated steatohepatitis (MASH), researchers have reported.
The findings address a clinical need, according to a group led by medical student Shyna Zhuoying Gunalan, of the National University of Singapore. Gunalan and colleagues published results from a literature review on January 13 in the American Journal of Gastroenterology.
"Non-invasive, accurate diagnostic tools are critical for identifying patients at risk [of MASH]," they noted.
MASH is an advanced form of MASLD and is characterized by damage to the liver, inflammation, and varying degrees of fibrosis. Patients considered to be at risk have metabolic dysfunction-associated steatohepatitis activity scores (MAS) equal to or greater than 4, on a scale of 0 to 8.
Gunalan and colleagues conducted a search of Medline and Embase (from the databases' inception to December 2024), identifying studies that reported on MRI-based diagnostic techniques for "at risk" MASH. Twenty studies that included 9,480 participants met the team's inclusion criteria. The group calculated sensitivity, specificity, and diagnostic odds ratios, and applied "ruling in" (TRI) and "ruling out" (TRO) thresholds for two techniques: Focused Abbreviated Survey MRI (FAST) and MR elastography (MRE) combined with the Fibrosis-4 (FIB-4) Index for Liver Fibrosis (MEFIB).
The investigators reported the following:
- The FAST technique showed the highest TRO sensitivity (87%) with moderate specificity (57%) and TRI specificity of 90% with reduced sensitivity (44%).
- MEFIB showed high TRO sensitivity (81%) but lower specificity (60%). Its TRI specificity was 87%, and its sensitivity was 50%.
"FAST with its accessibility and robust diagnostic performance may be well-suited for large-scale application," the group concluded.
Access the full study here.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
