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

Retrospective Cohort Study

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 4 Representative SHapley Additive exPlanation case examples, median (interquartile rage)/n (%)

Variable	Group characteristics		Value (SHAP value)
Variable	Fibrosis < 2 (n = 168)	Fibrosis ≥ 2 (n = 29)	Case 1	Case 2	Case 3	Case 4
LSM (kPa)	6.0 (4.6-8.0)	12.1 (8.0-20.9)	3.9 (-2.78)	4.9 (-3.29)	7.7 (-0.47)	5 (-3.34)
Graft age (year)	1.0 (0.3-2.3)	3.4 (2.6-8.9)	0.7 (-1.55)	4.0 (+0.62)	14.8 (+2.39)	4.7 (+0.77)
Age (year)	58.8 (49.7-63.0)	49.0 (38.8-56.1)	62.5 (-1.17)	59.0 (-1.43)	42.0 (+1.70)	59.0 (-1.4)
BMI	26.9 (25.8-31.1)	26.5 (24.5-27.6)	26.9 (+0.27)	21.3 (+0.80)	26.9 (+0.64)	24.5 (+0.92)
HGB	127.0 (113.5-136.0)	134.0 (122.0-145.0)	131 (+0.13)	144 (+0.34)	158 (+0.57)	124 (-0.11)
ALP	112.0 (85.7-163.0)	134.0 (108.0-229.0)	77 (-0.11)	125 (+0.35)	52 (-0.11)	175 (-0.21)
Living donor	62 (36.9)	6 (20.7)	No (+0.07)	No (+0.21)	No (+0.22)	No (+0.21)
AST	27.0 (19.7-41.2)	41.0 (28.0-58.0)	10 (-0.21)	29 (-0.12)	22 (-0.10)	38 (+0.14)
HTN pre-transplant (yes/no)	37 (22.0)	2 (6.9)	No (-0.04)	No (-0.01)	No (-0.01)	No (-0.01)

LSM: Liver stiffness measurement; BMI: Body mass index; HGB: Hemoglobin; ALP: Alkaline phosphatase; AST: Aspartate aminotransferase; HTN: Hypertension; SHAP: SHapley Additive exPlanation.

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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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