ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in MASLD.

Abstract

Introduction: The recent FDA-approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis (MASH) in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2.

Methods: The ALADDIN study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a Training set of 827 patients, a Testing Set of 504 patients with biopsy-confirmed MASLD from six centers, and an External Validation Set of 1,299 patients from 9 centers. Five algorithms were compared using AUC in the Test Set: ElasticNet (EN), Random Forest (RF), Gradient Boosting Machines (GBM), XGBoost (XGB), and Neural Networks (NN). The top three (RF, GBM, and XGB) formed an ensemble model.

Results: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% CI: 0.764-0.819), outperformed VCTE alone (0.745, 95% CI 0.717-0.772, p<0.0001)FAST (0.710, 0.679-0.748, p<0.0001) and Agile-3 model (0.740, 0.710-0.770, p<0.0001) in terms of the AUC, Decision Curve Analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668-0.749), outperfored Fibrosis-4 (FIB-4), steatosis-associated fibrosis estimator (SAFE), and LiverRisk scores.

Conclusions: Along with the SAFE model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined non-invasive approach to patient selection for resmetirom without the need for liver biopsy. Additionally, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.