Imaging-Based Modalities for Identifying At Risk MASH. A Diagnostic Test Meta-Analysis

Metabolic dysfunction-associated steatohepatitis (MASH) is an advanced form of metabolic dysfunction-associated steatotic liver disease, characterized by hepatocellular injury, inflammation, and varying degrees of fibrosis. Noninvasive, accurate diagnostic tools are critical for identifying patients at risk (metabolic activity score ≥4, F ≥ 2). This meta-analysis evaluates the diagnostic performance of imaging-based technologies for ruling-in (TRI) and ruling-out (TRO) at risk MASH.

A systematic search of Medline and Embase (inception to December 20, 2024) identified studies reporting on MRI-based diagnostic techniques for at risk MASH. Eligible studies were independently screened, with 20 studies meeting inclusion criteria. Sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated using bivariate meta-analysis, applying prespecified TRO and TRI thresholds to each technique.

Twenty studies involving 9,480 participants were included. FibroScan-aspartate aminotransferase (FAST) demonstrated highest TRO sensitivity (0.871) with moderate specificity (0.567) and TRI specificity (0.900) with reduced sensitivity (0.441). Magnetic resonance elastography plus fibrosis-4 achieved high TRO sensitivity (0.812) but lower specificity (0.606); TRI specificity was 0.872, sensitivity was 0.500. MRI-aspartate aminotransferase exhibited intermediate performance, while cT1 thresholds showed variable diagnostic accuracy. A sensitivity analysis of head-to-head studies shows superior performance in FAST compared with other diagnostic methods.

FAST with its accessibility and robust diagnostic performance may be well-suited for large-scale application. MRI-based techniques are effective noninvasive options for diagnosing at risk MASH in metabolic dysfunction-associated steatotic liver disease and may provide strong alternatives. Rather than challenging existing perspectives, this study provides a reflective overview of current evidence on imaging-based modalities for at risk MASH.