Liver stiffness-platelet ratio index and machine learning models for the noninvasive diagnosis of significant fibrosis in chronic hepatitis B

doi:10.4254/wjh.v18.i5.119798

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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. May 27, 2026; 18(5): 119798
Published online May 27, 2026. doi: 10.4254/wjh.v18.i5.119798

Table 1 Baseline characteristics of the training and validation datasets, n (%)/median (interquartile range)

Variables	Training set (n = 768)	Validation set (n = 330)	P values
Gender (male/female)	Male 473 (61.6)	Male 211 (63.9)	0.50
Gender (male/female)	Female 295 (38.4%)	Female 119 (36.1%)	0.50
Age (years)	36.00 (30.00, 44.25)	37.00 (30.25, 45.75)	0.56
Weight (kg)	61.00 (53.00, 69.00)	61.80 (54.00, 69.00)	0.85
Height (cm)	167.00 (160.00, 171.00)	168.00 (160.00, 173.00)	0.27
Hepatitis B virus DNA (log10 IU/mL)	5.90 (3.78, 7.63)	6.14 (3.56, 7.62)	0.59
Hepatitis B surface antigen (log10 IU/mL)	3.59 (3.07, 4.22)	3.57 (3.04, 4.32)	0.76
Hepatitis B surface antibody (mIU/mL)	0.19 (0.00, 1.10)	0.10 (0.00, 1.00)	0.21
Hepatitis B e antigen (COI)	0.59 (0.11, 700.00)	0.50 (0.17, 700.00)	0.86
Hepatitis B e antibody (COI)	0.32 (0.01, 13.39)	0.22 (0.01, 7.99)	0.26
Hepatitis B core antibody (COI)	7.95 (0.01, 9.27)	7.66 (0.01, 9.35)	0.71
White blood cell (10⁹/L)	5.30 (4.50, 6.20)	5.30 (4.40, 6.10)	0.84
Neutrophil Abs. count (10⁹/L)	2.90 (2.40, 3.60)	3.00 (2.40, 3.70)	0.45
Hemoglobin (g/L)	145.00 (132.00, 155.25)	146.00 (132.25, 156.00)	0.67
Platelet (10⁹/L)	202.00 (168.00, 239.00)	195.00 (165.00, 229.00)	0.02
Prothrombin time (seconds)	13.10 (12.62, 13.70)	13.10 (12.60, 13.70)	0.91
International normalized ratio	1.01 (0.97, 1.06)	1.02 (0.97, 1.06)	0.62
Albumin (g/L)	44.00 (42.00, 46.00)	44.00 (42.00, 46.00)	0.52
Globulin (g/L)	31.00 (28.00, 34.00)	30.00 (27.00, 34.00)	0.62
Total bilirubin (μmol/L)	16.05 (11.80, 21.50)	15.50 (12.10, 21.10)	0.88
Direct bilirubin (μmol/L)	4.85 (3.00, 7.20)	4.80 (2.90, 7.47)	0.92
Alanine aminotransferase (U/L)	68.00 (44.00, 116.00)	68.00 (47.00, 118.00)	0.65
Aspartate aminotransferase (U/L)	49.00 (27.75, 94.00)	44.00 (25.00, 89.75)	0.17
Gamma-glutamyl transferase (U/L)	33.00 (21.00, 62.00)	31.50 (21.00, 56.00)	0.44
Alkaline phosphatase (U/L)	63.00 (33.00, 83.00)	61.00 (31.25, 81.00)	0.64
Alpha-fetoprotein (ng/mL)	2.95 (1.75, 5.55)	2.79 (1.82, 4.64)	0.51
Liver stiffness measurement (kPa)	7.40 (5.60, 10.60)	7.30 (5.50, 10.20)	0.70

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Table 2 Detailed model performance metrics for liver fibrosis diagnostic models

Metric	Liver stiffness-platelet ratio index	Fibrosis-4	AST to platelet ratio index	Gamma-glutamyl transpeptidase to platelet ratio	S-Index	Hui model	Age-male-ALP-platelets risk score	AST to platelet and age-gender model
Training data
AUROC	0.84	0.65	0.66	0.70	0.71	0.75	0.69	0.71
95%CI lower	0.80	0.61	0.62	0.66	0.67	0.71	0.65	0.66
95%CI upper	0.87	0.69	0.70	0.74	0.75	0.78	0.74	0.75
UA	0.06	0.09	0.08	0.08	0.08	0.08	0.09	0.09
Matthews correlation coefficient	0.46	0.21	0.20	0.26	0.26	0.33	0.27	0.31
Youden index (%)	52.96	20.38	22.87	28.58	30.00	37.87	31.13	33.25
PPV (%)	47.55	40.56	32.08	40.39	36.54	40.29	38.04	44.83
NPV (%)	92.53	80.44	85.04	83.43	85.78	88.08	85.52	84.33
Sensitivity (%)	82.45	38.83	72.87	54.79	68.62	72.87	65.96	55.32
Specificity (%)	70.52	81.55	50.00	73.79	61.38	65.00	65.17	77.93
Accuracy (%)	73.44	71.09	55.60	69.14	63.15	66.93	65.36	72.40
TP (%)	20.18	9.51	17.84	13.41	16.80	17.84	16.15	13.54
Validation data
AUROC	0.83	0.65	0.61	0.71	0.71	0.74	0.70	0.73
95%CI lower	0.78	0.58	0.54	0.64	0.64	0.67	0.63	0.67
95%CI upper	0.88	0.72	0.68	0.78	0.78	0.79	0.76	0.80
UA	0.10	0.14	0.13	0.13	0.13	0.12	0.13	0.13
Matthews correlation coefficient	0.43	0.23	0.08	0.30	0.26	0.29	0.26	0.30
Youden index (%)	49.65	23.45	9.26	31.74	30.02	33.75	30.02	31.72
PPV (%)	44.97	41.67	27.91	44.00	36.60	37.50	36.60	44.79
NPV (%)	92.22	81.22	78.98	83.84	85.80	87.57	85.80	83.69
Sensitivity (%)	82.72	43.21	59.26	54.32	69.14	74.07	69.14	53.09
Specificity (%)	66.94	80.24	50.00	77.42	60.89	59.68	60.89	78.63
accuracy (%)	70.82	71.12	52.28	71.73	62.92	63.22	62.92	72.34
TP (%)	20.36	10.64	14.59	13.37	17.02	18.24	17.02	13.07

AST: Aspartate aminotransferase; AUROC: Area under the receiver operating characteristic curve; NPV: Negative predictive value; PPV: Positive predictive value; TP: True positives; UA: Uncertainty area.

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Table 3 Performance of machine learning and deep learning models with 26 features and liver stiffness-platelet ratio index + 26 features on imbalanced dataset

Models	Area under the receiver operating characteristic curve	Youden	Positive predictive value (%)	Negative predictive value (%)	Accuracy (%)	True positives (n)	False positive (n)	True negative (n)	False negative (n)	Sensitivity (%)	Specificity (%)
Using 26 features
LogisticRegression	0.85	0.58	0.57	0.92	0.80	41.40	31.60	134.00	12.40	0.77	0.81
SVM	0.83	0.55	0.55	0.91	0.79	40.40	33.40	132.20	13.40	0.75	0.80
NuSVC	0.81	0.52	0.51	0.91	0.76	41.40	40.80	124.80	12.40	0.77	0.75
DecisionTree	0.70	0.40	0.54	0.85	0.77	29.80	25.60	140.00	24.00	0.55	0.85
ExtraTree	0.63	0.27	0.44	0.82	0.72	24.80	32.00	133.60	29.00	0.46	0.81
GaussianNB	0.80	0.49	0.52	0.89	0.77	38.20	35.80	129.80	15.60	0.71	0.78
GradientBoosting	0.88	0.63	0.60	0.93	0.82	43.20	29.00	136.60	10.60	0.80	0.82
HistGradientBoosting	0.87	0.62	0.55	0.94	0.79	45.20	37.00	128.60	8.60	0.84	0.78
AdaBoost	0.86	0.61	0.54	0.95	0.78	46.20	41.60	124.00	7.60	0.86	0.75
RandomForest	0.87	0.62	0.58	0.93	0.81	44.00	32.00	133.60	9.80	0.82	0.81
KNeighbors	0.77	0.43	0.47	0.89	0.72	38.00	46.40	119.20	15.80	0.71	0.72
KAN	0.83	0.55	0.56	0.91	0.79	40.80	34.20	131.40	13.00	0.76	0.79
NeuralNetwork	0.84	0.57	0.55	0.92	0.78	42.80	36.80	128.80	11.00	0.80	0.78
Using liver stiffness-platelet ratio index + 26 features
LogisticRegression	0.85	0.58	0.58	0.91	0.81	40.80	29.00	136.60	13.00	0.76	0.82
SVM	0.83	0.55	0.54	0.91	0.78	41.00	34.80	130.80	12.80	0.76	0.79
NuSVC	0.81	0.53	0.54	0.91	0.77	40.40	37.20	128.40	13.40	0.75	0.78
DecisionTree	0.67	0.35	0.51	0.84	0.76	26.80	25.00	140.60	27.00	0.50	0.85
ExtraTree	0.66	0.32	0.48	0.83	0.75	26.40	28.40	137.20	27.40	0.49	0.83
GaussianNB	0.81	0.52	0.58	0.89	0.80	37.20	27.80	137.80	16.60	0.69	0.83
GradientBoosting	0.87	0.62	0.56	0.94	0.80	44.80	34.80	130.80	9.00	0.83	0.79
HistGradientBoosting	0.87	0.61	0.53	0.94	0.78	46.00	41.40	124.20	7.80	0.86	0.75
AdaBoost	0.85	0.58	0.59	0.92	0.80	42.00	33.00	132.60	11.80	0.78	0.80
RandomForest	0.86	0.64	0.62	0.93	0.82	43.60	28.80	136.80	10.20	0.81	0.83
KNeighbors	0.78	0.42	0.47	0.88	0.72	37.00	44.20	121.40	16.80	0.69	0.73
KAN	0.84	0.58	0.55	0.92	0.79	42.60	35.80	129.80	11.20	0.79	0.78
NeuralNetwork	0.85	0.59	0.59	0.92	0.81	41.00	28.80	136.80	12.80	0.76	0.83

SVM: Support vector machine; NuSVC: Nu-support vector classification; KAN: Kolmogorov-Arnold Network.

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Citation: Lin JY, Ai ZX, Luo MJ, Su LZ, Gao XG, Jiang HL, Lin JQ, Zhang HY, Sun YY, Yu HT, Zhang L, Gong XQ. Liver stiffness-platelet ratio index and machine learning models for the noninvasive diagnosis of significant fibrosis in chronic hepatitis B. World J Hepatol 2026; 18(5): 119798
URL: https://www.wjgnet.com/1948-5182/full/v18/i5/119798.htm
DOI: https://dx.doi.org/10.4254/wjh.v18.i5.119798

Lin JY, Ai ZX, Luo MJ, Su LZ, Gao XG, Jiang HL, Lin JQ, Zhang HY, Sun YY, Yu HT, Zhang L, Gong XQ. Liver stiffness-platelet ratio index and machine learning models for the noninvasive diagnosis of significant fibrosis in chronic hepatitis B. World J Hepatol 2026; 18(5): 119798 [DOI: 10.4254/wjh.v18.i5.119798]

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