Evaluating the efficacy of 8 non-invasive models in predicting MASLD and progression: a prospective study
Aruhan Yang, Xiaoxue Zhu, Lei Zhang, Dezhi Zhang, Meishan Jin, Guoyue Lv, Yanhua Ding
Abstract
Selecting the optimal non-invasive diagnostic model for MASLD (Metabolic Dysfunction-Associated Steatosis Liver Disease) and steatosis progression is a critical issue given the variety of available models. We aimed to compare the performance of eight clinical prediction models for diagnosing and predicting the progression of hepatic steatosis using MRI-PDFF (Magnetic Resonance Imaging-Derived Proton Density Fat Fraction), and validate the findings with FibroScan and histopathological results. In this study, 846 participants were initially enrolled, with 108 undergoing liver biopsy and 706 completing one-year follow-up, including 26 who underwent repeat biopsy. We calculated scores for eight clinical prediction models (FAST, KNAFLD, HSI, FLI, Liver Fat Score, Liver Fat Equation, BAAT, LAP) using collected clinical data and defined steatosis progression as a 30% relative increase in liver fat content (LFC) measured by MRI-PDFF. CAP(Controlled Attenuation Parameter) and LSM (Liver Stiffness Measurement) were obtained by Fibroscan. MRI-PDFF served as the reference standard for evaluating model accuracy, and sensitivity analyses were performed using liver biopsy and Fibroscan results. Among the eight clinical models, NAS (nonalcoholic fatty liver disease activity score) showed higher correlation with the FAST and KNAFLD models (r: 0.62 and 0.52, respectively). Among the whole cohort (N = 846), KNAFLD was the best model for predicting different degrees of hepatic steatosis (AUC = 0.84). When the KNAFLD score was above 2.935, LFC was significantly higher (4.4% vs. 19.7%, P < 0.001). After 1 year of follow-up (N = 706), FAST performed best in predicting MASLD progression (AUC = 0.84); with dFAST > -0.02, LFC increased (8.6–10.9%, P < 0.05), mean LSM increased by 0.51 kPa, and with dFAST < -0.02, LFC significantly decreased (11.5–8.5%, P < 0.05), mean LSM and NAS decreased by 0.87 kPa and 0.76, respectively (both P < 0.05). Most models demonstrated good diagnostic and prognostic capabilities for hepatic steatosis, with FAST and KNAFLD showing particular promise as primary non-invasive tools in clinical practice. Chinese Clinical Trial Registry NO: ChiCTR2100054743, Registered December 26, 2021. Numerous blood and imaging biomarkers have been developed to diagnose MASLD, which can progress to MASH, cirrhosis, and HCC. Early diagnosis and long-term follow-up using noninvasive methods are essential. Comparison of the accuracy of 8 noninvasive methods to predict liver steatosis and its progression based on MRI-PDFF in the 1-year prospective study revealed that most noninvasive techniques are correlated with LFC (Liver Fat Content) and have an acceptable accuracy to estimate the degree and progression of hepatic steatosis. KNAFLD has the best accuracy in predicting MASLD degree. FAST has the best accuracy in predicting steatosis progression. KNAFLD performed best in predicting MASLD and discriminating different degrees of hepatic steatosis, which could be used as a reference in clinical diagnosis. For a long-term follow-up study in MASLD patients, the changes in FAST or KNAFLD scores might be a better method to predict the status of steatosis progression.