Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables

Abstract

Background: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom and other drugs in clinical trials. We aimed to validate and re-optimize known NITs, and most importantly to develop new machine learning (ML)-based NITs to accurately detect MASH F2-F3.

Methods: Clinical and metabolomic data were collected from 443 patients across three countries and two clinic types (metabolic surgery, gastroenterology/hepatology) covering the entire spectrum of biopsy-proven MASH, including cirrhosis and healthy controls. Three novel types of ML models were developed using a categorical gradient boosting machine pipeline under a classic 4:1 split and a secondary independent validation analysis. These were compared with twenty-three biomarker, imaging, and algorithm-based NITs with both known and re-optimized cutoffs for MASH F2-F3.

Results: The NAFLD (Non-Alcoholic Fatty Liver Disease) Fibrosis Score (NFS) at a - 1.455 cutoff attained an area under the receiver operating characteristic curve (AUC) of 0.59, the highest sensitivity (90.9 %), and a negative predictive value (NPV) of 87.2 %. FIB-4 risk stratification followed by elastography (8 kPa) had the best specificity (86.9 %) and positive predictive value (PPV) (63.3 %), with an AUC of 0.57. NFS followed by elastography improved the PPV to 65.3 % and AUC to 0.62. Re-optimized FibroScan-AST (FAST) at a 0.22 cutoff had the highest PPV (69.1 %). ML models using aminotransferases, metabolic syndrome components, BMI, and 3-ureidopropionate achieved an AUC of 0.89, which further increased to 0.91 following hyperparameter optimization and the addition of alpha-ketoglutarate. These new ML models outperformed all other NITs and displayed accuracy, sensitivity, specificity, PPV, and NPV up to 91.2 %, 85.3 %, 97.0 %, 92.4 %, and 90.7 % respectively. The models were reproduced and validated in a secondary sensitivity analysis, that used one of the cohorts as feature selection/training, and the rest as independent validation, likewise outperforming all other applicable NITs.

Conclusions: We report for the first time the diagnostic characteristics of non-invasive, metabolomics-based biomarker models to detect MASH with fibrosis F2-F3 required for Resmetirom treatment and inclusion in ongoing phase-III trials. These models may be used alone or in combination with other NITs to accurately determine treatment eligibility.

Keywords: MASH F2-F3; MASLD; Machine learning; Metabolomics; NAFLD; Non-invasive tests; Resmetirom.