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Updated: Jun 19, 2026

Optimized Analysis of In Vivo and In Vitro Hepatic Steatosis
Published on: March 11, 2017
Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction-associated steatohepatitis
Shi Qi1, Xiaodie Wei1, Jinhan Zhao1
1Beijing Youan Hospital, Capital Medical University, Beijing, China.
Background And Aim:
To identify individuals with metabolic dysfunction-associated steatohepatitis (MASH) or "at-risk" MASH among patients with metabolic dysfunction-associated steatotic liver disease (MASLD), three noninvasive models are available with satisfactory efficiency, which include magnetic resonance imaging [MRI]- AST (MAST), FibroScan-AST (FAST score), and magnetic resonance elastography [MRE] plus FIB-4 (MEFIB). We aimed to evaluate the most accurate approach for diagnosing MASH or "at-risk" MASH.
Methods:
We included 108 biopsy-proven MASLD patients who underwent simultaneous assessment of MRE, MRI proton density fat fraction (MRI-PDFF), and FibroScan scans. Compared with the histological diagnosis, we analyzed the AUC of each model and assessed the accuracy.
Results:
Our study cohort consisted of 64.8% of MASH and 25.9% of "at-risk" MASH. When analyzing the performance of each model for the diagnostic accuracy of MASH, we found that the AUC [95% CI] of MAST was comparable to FAST (0.803 [0.719-0.886] vs 0.799 [0.707-0.891], P = 0.930) and better than MEFIB (0.671 [0.571-0.772], P = 0.005). Similar findings were observed in the "at-risk" MASH patients. The AUCs [95% CI] for MAST, FAST, and MEFIB were 0.810 [0.719-0.900], 0.782 [0.689-0.874], and 0.729 [0.619-0.838], respectively. The models of MAST and FAST had comparable AUCs (P = 0.347), which were statistically significantly higher than that of MEFIB (P = 0.041). Additionally, the cutoffs for diagnosis of MASH were lower than "at-risk" MASH.
Conclusion:
MAST and FAST performed better than MEFIB in diagnosing "at-risk" MASH and MASH using lower cutoff values. Our findings provided evidence for selecting the most accurate noninvasive model to identify patients with MASH or at-risk MASH.
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