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

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. 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. 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. 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.