Novel Machine-Learning Models Outperform Noninvasive Tests in Metabolic Dysfunction-Associated Steatohepatitis With Fibrosis.

BACKGROUND & AIMS: Noninvasive tests (NITs) have not yet been systematically evaluated or optimized to detect metabolic dysfunction-associated steatohepatitis (MASH) with fibrosis stages F2 to F3, the current treatment-eligible group defined by the United States Food and Drug Administration for emerging treatments, or, separately, for cirrhosis (F4). We aimed to assess the performance of existing and novel approaches in identifying the target F2 to F3 population and excluding cirrhosis. METHODS: We analyzed a cohort of 905 biopsy-proven individuals (including healthy controls) and evaluated 28 biomarker-, imaging-, or algorithm-based NITs that have been previously tested for MASH F2 to F4, using published and newly optimized cutoffs. In parallel, we developed and validated machine learning (ML) models using categorical gradient boosting that selected routine variables and hormonal markers. Performance was assessed in a held-out validation cohort and a secondary setting using one population for training/feature selection and two for independent validation. RESULTS: Previous NITs did not perform optimally for F2 to F3 detection, the highest being fibroscan-AST (FAST) with an area under the curve (AUC) of 0.67; negative predictive value of 92%; and positive predictive value of 30%. In contrast, novel ML models achieved AUCs of up to 0.86 for F2 to F3 in validation, with improved positive predictive value up to 65% and overall accuracy up to 87%. For cirrhosis (F4), AGILE4+ showed the highest sensitivity (92%) and negative predictive value (99.7%) among known NITs, whereas ML models reached a mean AUC of 0.93 (accuracy = 86%), and outperformed conventional tools. CONCLUSIONS: Novel ML models significantly improve the detection of MASH F2 to F3 and cirrhosis over existing NITs and address a key diagnostic gap in the United States Food and Drug Administration-defined treatment window. These tools may support patient selection for emerging therapies, as well as for clinical trial enrolment.