{"doi":"10.1111/jgh.16589","title":"Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction‐associated steatohepatitis","abstract":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Background and Aim</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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] <jats:italic>vs</jats:italic> 0.799 [0.707–0.891], <jats:italic>P</jats:italic> = 0.930) and better than MEFIB (0.671 [0.571–0.772], <jats:italic>P</jats:italic> = 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 (<jats:italic>P</jats:italic> = 0.347), which were statistically significantly higher than that of MEFIB (<jats:italic>P</jats:italic> = 0.041). Additionally, the cutoffs for diagnosis of MASH were lower than “at‐risk” MASH.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>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.</jats:p></jats:sec>","journal":"Journal of Gastroenterology and Hepatology","year":2024,"id":630193,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1632452,"name":"Xiaodie Wei","orcid":null,"position":1,"is_corresponding":false},{"id":1632453,"name":"Jinhan Zhao","orcid":null,"position":2,"is_corresponding":false},{"id":1632454,"name":"Xinhuan Wei","orcid":null,"position":3,"is_corresponding":false},{"id":1632456,"name":"Haiqing Guo","orcid":null,"position":4,"is_corresponding":false},{"id":1632457,"name":"Jingxian Hu","orcid":null,"position":5,"is_corresponding":false},{"id":1632458,"name":"Qiqige WuYun","orcid":null,"position":6,"is_corresponding":false},{"id":291460,"name":"Calvin Q. Pan","orcid":"0000-0002-3723-6688","position":7,"is_corresponding":false},{"id":1632459,"name":"Nengwei Zhang","orcid":null,"position":8,"is_corresponding":false},{"id":277422,"name":"Jing Zhang","orcid":"0000-0002-3082-8330","position":9,"is_corresponding":false},{"id":1467452,"name":"Shi Qi","orcid":"0000-0001-5930-4541","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Performance of MAST, FAST, and MEFIB in predicting metabolic dysfunction‐associated steatohepatitis","abstract":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Background and Aim</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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] <jats:italic>vs</jats:italic> 0.799 [0.707–0.891], <jats:italic>P</jats:italic> = 0.930) and better than MEFIB (0.671 [0.571–0.772], <jats:italic>P</jats:italic> = 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 (<jats:italic>P</jats:italic> = 0.347), which were statistically significantly higher than that of MEFIB (<jats:italic>P</jats:italic> = 0.041). Additionally, the cutoffs for diagnosis of MASH were lower than “at‐risk” MASH.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>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.</jats:p></jats:sec>","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38686620","pmcid":null,"openalex_id":"https://openalex.org/W4396519251","authors":[],"funders":[{"funder_name":"Beijing Municipal Administration of Hospitals Incubating Program","grant_id":"PX2023061","title":null},{"funder_name":"Beijing Municipal Administration of Hospitals Incubating Program","grant_id":"PX2024059","title":null},{"funder_name":"Scientific Research Project of Beijing Youan Hospital","grant_id":"BJYAYY-YN2022-21","title":null},{"funder_name":"Beijing Municipal Administration of Hospitals Incubating Program","grant_id":"PX2024062","title":null},{"funder_name":"Scientific Research Project of Beijing Youan Hospital","grant_id":"BJYAYY-YN2022-17","title":null},{"funder_name":"Scientific Research Project of Beijing Youan Hospital","grant_id":"BJYAYY-YN2022-26","title":null},{"funder_name":"Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support","grant_id":"YGLX202339","title":null},{"funder_name":"Reform and Development Project of Beijing Institute of Hepatology","grant_id":"Y-KF2023-4","title":null},{"funder_name":"Capitals' Funds for Health Improvement and Research","grant_id":"2022-2Z-2187","title":null}],"total_grants":9,"fwci":3.439,"citation_percentile":0.9342061,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":6},{"year":2026,"count":5}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jgh.16589","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jgh.16589","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/jgh.16589","host_type":"publisher"},{"url":"https://doi.org/10.1111/jgh.16589","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38686620","host_type":"repository"}],"fields_of_study":["Liver Disease Diagnosis and Treatment","Diabetes, Cardiovascular Risks, and Lipoproteins","Liver Disease and Transplantation","Humans","Elasticity Imaging Techniques","Female","Male","Middle Aged","Magnetic Resonance Imaging","Fatty Liver","Predictive Value of Tests","Adult","Aged","Metabolic Diseases","Aspartate Aminotransferases","Risk"],"mesh_terms":["Adult","Aged","Aspartate Aminotransferases","Fatty Liver","Female","Humans","Magnetic Resonance Imaging","Male","Metabolic Diseases","Middle Aged","Predictive Value of Tests","Risk","Elasticity Imaging Techniques"],"keywords":["Medicine","Steatohepatitis","Magnetic resonance imaging","Fatty liver","Internal medicine","Cohort","Gastroenterology","Magnetic resonance elastography","Metabolic syndrome","Elastography","Radiology","Disease","Ultrasound","Obesity","Masld","At‐risk Mash, Noninvasive Diagnosis Model","Metabolic Dysfunction‐associated Liver Diseases"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T20:39:03.747302Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}