This study investigates the diagnostic accuracy of liver fat fraction (FF) and other biomarkers in differentiating metabolic dysfunction‐associated steatohepatitis (MASH) from non‐MASH conditions in a cohort of 127 liver transplant patients using 1H MRS and machine learning techniques. Receiver operating characteristic analysis identified FF as the most significant predictor, achieving an area under the curve (AUC) > 0.96 for distinguishing MASH from non‐steatosis and non‐MASH metabolic dysfunction‐associated steatotic liver disease (MASLD). Secondary biomarkers, including insulinemia and elastography, showed moderate discriminatory power (AUC = 0.7–0.8) and contributed to refining classification decisions within a decision tree model. The decision tree analysis, validated with 10‐fold cross‐validation and independent testing, demonstrated robust sensitivity and specificity, with FF contributing 60%–70% to decision‐making. Secondary splits, such as insulinemia (~16.21 μIU/mL) and elastography (~8 kPa), provided additional discriminatory power, particularly in cases with borderline FF values. Non‐significant biomarkers, such as waist circumference and signals of diallylic protons resonating at 2.8 ppm, were excluded due to low discriminatory performance (AUC < 0.7). Compared to the general population (~5.8% prevalence), MASH was significantly more common in liver transplant recipients (~30%–50%). In patients with FF > 5.3%, the positive predictive value (PPV) for MASH ranged from 88% to 97%, more than twice the PPV observed in the general population (approximately 60%). These findings align with existing literature validating MRI‐derived proton density fat fraction as a reliable biomarker for hepatic steatosis. However, liver fat percentage alone is insufficient for MASH diagnosis. Secondary biomarkers, particularly insulinemia and elastography, enhanced classification accuracy near the FF threshold of 5.3%. This multiparametric approach significantly improves diagnostic accuracy and addresses the elevated risk and unique clinical needs of liver transplant recipients. Overall, these results underscore the clinical utility and precision of MR spectroscopy as a noninvasive biomarker for MASH diagnosis in liver transplant patients.

Machine learning identifies liver fat fraction (FF) measured by 1H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers for diagnosing steatohepatitis in liver transplant patients, validated through decision tree analysis. Compared to the general population (~5.8% prevalence), MASH is significantly more common in liver transplant recipients (~30%-50%). In patients with FF > 5.3%, the positive predictive value for MASH ranged up to 97%, more than twice the value observed in the general population.

Department of Hepatogastroenterology Institute for Clinical and Experimental Medicine Prague Czech Republic

MR Unit Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Prague Czech Republic

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