Kubina, Matthew
Mayo Clinic
Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA.
Citations 1
ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in Metabolic Dysfunction-Associated Steatotic Liver Disease
Alkhouri, Naim , Yip, Terry Cheuk‐Fung , Takawy, Marina , Adams, Leon A. , Jafri, Syed‐Mohammed , Zhong, Bihui , Chen, Vincent , Dunn, Nicholas , Nadeem, Rida, Wang, Zhengyi, Duseja, Ajay , Almahanna, Yousef , Omeish, Haya , Ye, Junzhao , Harrison, Stephen A. , Cristiu, Jessica , Robert, Sage, Bajunayd, Amani, Shao, Congxiang , Kubina, Matthew , Cheuk-Fung Yip, Terry , Castera, Laurent , Takawy, Marina , Verma, Nipun , Jafri, Syed-Mohammed , Chen, V L , Singal, Ashwani K , Díaz, Luis A , Dunn, Nicholas , Wong, Vincent Wai-Sun , Abdelmalek, Manal F. , Wang, Zhengyi , Duseja, Ajay , Almahanna, Yousef , Ye, Junzhao , Cristiu, Jessica , Arrese, Marco , Lai-Hung Wong, Grace , Kubina, Matthew , Dunn, Winston , Verma, Nipun , Arab, Juan Pablo , Zhong, Bihui , Dubourg, Julie , Singal, Ashwani K. , Díaz, Luis Antonio , Wong, Vincent Wai‐Sun , Omeish, Haya A. , Harrison, Stephen A. , Arrese, Marco , Wong, Grace Lai‐Hung , Dunn, Winston
American Journal of Gastroenterology | March 30, 2025 | Volume: 121 | Issue: 2 | Pages: 362-374
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.