Keywords: Liver, biology, models, methods
Motivation: Non-invasive identification of Metabolic dysfunction-associated steatohepatitis (MASH) are essential.Due to the limitations of existing non-invasive models, it is necessary to explore a more accurate identification model.
Goal(s): To explore a novel model based on multi-parameter MRI indicators in the identification of MASH.
Approach: Of the 84 patients who met the study requirements, 68 were diagnosed with MASH. The SPSS Statistics software was used to construct the model.
Results: MEPT1 model constructed by MRE, PDFF and T1mapping was the best identification model with satisfactory fitting and high identification performance and recognition ability (AUC 0.899).
Impact: The MEPT1 model based on multi-parameter MRI is more accurate and superior to MAST score in noninvasive identification of MASH patients, which can assist clinical identification and evaluation of MASH patients.
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