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 1 Patient demographic and clinical characteristics, median (interquartile rage)/n (%)

Variable	Fibrosis < 2, (n = 168)	Fibrosis ≥ 2, (n = 29)	P value	Missingness
Age at transplant	58.8 (49.7-63.0)	49.0 (38.8-56.1)	< 0.001	0 (0.0)
Graft age	1.0 (0.3-2.3)	3.4 (2.6-8.9)	< 0.001	5 (2.5)
Male	107 (63.7)	18 (62.1)	0.867	0 (0.0)
Body mass index	26.9 (25.8-31.1)	26.5 (24.5-27.6)	0.032	0 (0.0)
Type of transplant: Living donor	62 (36.9)	6 (20.7)	0.091	0 (0.0)
Fibroscan
CAP	258.2 (211.7-318.2)	240.0 (194.0-290.0)	0.087	7 (3.5)
LSM (kPa)	6.0 (4.6-8.0)	12.1 (8.0-20.9)	< 0.001	2 (1.0)
Albumin	41.0 (38.0-44.0)	41.0 (36.0-42.0)	0.187	0 (0.0)
Bilirubin	12.0 (9.0-18.2)	15.0 (12.0-22.0)	0.065	0 (0.0)
INR	1.0 (1.0-1.1)	1.1 (1.0-1.2)	0.405	0 (0.0)
ALP	112.0 (85.7-163.0)	134.0 (108.0-229.0)	0.001	0 (0.0)
ALT	36.5 (23.0-64.2)	52.0 (28.0-67.0)	0.713	0 (0.0)
AST	27.0 (19.7-41.2)	41.0 (28.0-58.0)	0.012	0 (0.0)
Creatinine	102.5 (86.0-126.0)	90.0 (84.0-110.0)	0.224	0 (0.0)
Hemoglobin	127.0 (113.5-136.0)	134.0 (122.0-145.0)	0.017	0 (0.0)
Platelet	164.0 (131.7-199.5)	156.0 (112.0-225.0)	0.618	0 (0.0)
White blood cells	5.9 (4.4-7.3)	6.2 (4.3-7.7)	0.916	0 (0.0)
MELD	9.0 (7.0-11.0)	10.0 (7.0-12.0)	0.082	0 (0.0)
Indication for transplant
AIH	2 (1.2)	2 (6.9)	0.044	0 (0.0)
ALF	0 (0.0)	0 (0.0)		0 (0.0)
ALD	19 (11.3)	5 (17.2)	0.369	0 (0.0)
HBV	11 (6.5)	1 (3.4)	0.521	0 (0.0)
HCV	44 (26.2)	9 (31.0)	0.589	0 (0.0)
Malignancy	0 (0.0)	0 (0.0)		0 (0.0)
Other	37 (22.0)	8 (27.6)	0.512	0 (0.0)
Unknown	6 (3.6)	1 (3.4)	0.973	0 (0.0)
Pre-transplant cardiovascular disease (yes/no)	34 (20.2)	7 (24.1)	0.635	0 (0.0)
Pre-transplant hypertension	37 (22.0)	2 (6.9)	0.059	0 (0.0)
Pre-transplant diabetes	35 (20.8)	2 (6.9)	0.076	0 (0.0)
Post-transplant hypertension	113 (67.2)	19 (65.5)	0.854	0 (0.0)
Post-transplant diabetes	117 (69.6)	18 (62.1)	0.419	0 (0.0)

P-values were calculated using χ² tests for categorical variables and Mann-Whitney U tests for continuous variables. CAP: Controlled attenuation parameter; LSM: Liver stiffness measurement; INR: International normalized ratio; ALP: Alkaline phosphatase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; MELD: Model for end-stage liver disease; AIH: Autoimmune hepatitis; ALF: Acute liver failure; ALD: Alcohol-related liver disease; HBV: Hepatitis B virus; HCV: Hepatitis C virus.

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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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Table 3 Machine learning models used for graft fibrosis classification using liver stiffness measurement value alone

Model	AUROC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)
Logistic regression	0.796 (0.719-0.936)	0.500 (0.029-0.833)	0.735 (0.759-1.000)
Support vector machine	0.787 (0.226-0.837)	0.833 (0.000-0.429)	0.618 (0.898-1.000)
Random forest	0.843 (0.649-0.898)	0.833 (0.200-0.875)	0.676 (0.649-0.898)
XGBoost	0.865 (0.669-0.902)	0.833 (0.133-0.817)	0.647 (0.740-0.958)

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

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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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