Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation

doi:10.4254/wjh.120258

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World Journal of Hepatology

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1948-5182

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Hepatol. Jun 27, 2026; 18(6): 120258
Published online Jun 27, 2026. doi: 10.4254/wjh.120258

Table 2 Multimodal machine learning models used for graft fibrosis classification using all variables

Model	AUROC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)
Logistic regression	0.828 (0.748-0.953)	0.667 (0.190-1.000)	0.882 (0.738-0.979)
Support vector machine	0.850 (0.756-0.973)	0.500 (0.222-1.000)	0.912 (0.810-1.000)
Random forest	0.922 (0.777-0.984)	0.667 (0.454-1.000)	0.941 (0.752-0.962)
XGBoost	0.927 (0.799-0.996)	0.667 (0.369-1.000)	0.941 (0.860-1.000)

XGBoost: Extreme gradient boosting; AUROC: Area under the receiver operating characteristic curve; CI: Confidence interval.

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Citation: Koivu A, Azarfar G, Shojaee M, Hlaing NKT, Rizvi S, Sharma D, Maleki S, Bhat M. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World J Hepatol 2026; 18(6): 120258
URL: https://www.wjgnet.com/1948-5182/full/v18/i6/120258.htm
DOI: https://dx.doi.org/10.4254/wjh.120258

Koivu A, Azarfar G, Shojaee M, Hlaing NKT, Rizvi S, Sharma D, Maleki S, Bhat M. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World J Hepatol 2026; 18(6): 120258 [DOI: 10.4254/wjh.120258]

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