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Randomized Controlled Trial
. 2021 Jul;28(7):593-603.
doi: 10.1002/jhbp.972. Epub 2021 May 12.

Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD

Gong Feng  1 , Kenneth I Zheng  2 , Yang-Yang Li  3 , Rafael S Rios  2 , Pei-Wu Zhu  4 , Xiao-Yan Pan  5 , Gang Li  2 , Hong-Lei Ma  2 , Liang-Jie Tang  2 , Christopher D Byrne  6 , Giovanni Targher  7 , Na He  8 , Man Mi  1 , Yong-Ping Chen  2   9   10 , Ming-Hua Zheng  2   9   10
Affiliations
Randomized Controlled Trial

Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD

Gong Feng et al. J Hepatobiliary Pancreat Sci. 2021 Jul.

Abstract

Background: The presence of significant liver fibrosis is a key determinant of long-term prognosis in non-alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non-invasive fibrosis biomarkers.

Methods: We used a cohort of 553 adults with biopsy-proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F ≥ 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm.

Results: In the training cohort, the variables selected by LASSO algorithm were body mass index, pro-collagen type III, collagen type IV, aspartate aminotransferase and albumin-to-globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869-0.904) for identifying fibrosis F ≥ 2. The LRM AUROC was 0.764, 95% CI 0.710-0.816 and significantly better than the AST-to-Platelet ratio (AUROC 0.684, 95% CI 0.605-0.762), FIB-4 score (AUROC 0.594, 95% CI 0.503-0.685) and NAFLD Fibrosis Score (AUROC 0.557, 95% CI 0.470-0.644). In the validation cohort, MLA also showed the highest AUROC (0.893, 95% CI 0.864-0.901). The diagnostic accuracy of MLA outperformed that of LRM in all subgroups considered.

Conclusions: Our newly developed MLA algorithm has excellent diagnostic performance for predicting fibrosis F ≥ 2 in patients with biopsy-confirmed NAFLD.

Keywords: NAFLD; diagnosis; fibrosis; liver biopsy; machine learning algorithm.

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References

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