ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in Metabolic Dysfunction-Associated Steatotic Liver Disease
Recommended Citation
Alkhouri N, Cheuk-Fung Yip T, Castera L, Takawy M, Adams LA, Verma N, Arab JP, Jafri SM, Zhong B, Dubourg J, Chen VL, Singal AK, Díaz LA, Dunn N, Nadeem R, Wai-Sun Wong V, Abdelmalek MF, Wang Z, Duseja A, Almahanna Y, Omeish HA, Ye J, Harrison SA, Cristiu J, Arrese M, Robert S, Lai-Hung Wong G, Bajunayd A, Shao C, Kubina M, and Dunn W. ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in MASLD. Am J Gastroenterol 2025.
Document Type
Article
Publication Date
3-27-2025
Publication Title
The American journal of gastroenterology
Abstract
INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥ F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥ F2.
METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (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 metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) 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% confidence interval [CI]: 0.764-0.819), outperformed VCTE alone (0.745, 95% CI 0.717-0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679-0.748, P < 0.0001), and Agile-3 model (0.740, 0.710-0.770, P < 0.0001) regarding 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) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores.
DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE ( https://aihepatology.shinyapps.io/ALADDIN1 ) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab ( https://aihepatology.shinyapps.io/ALADDIN2 ) offers an effective alternative when VCTE is unavailable.
PubMed ID
40146016
ePublication
ePub ahead of print
