Value of two-dimensional shear wave elastography combined with serological markers in the differential diagnosis of primary biliary cholangitis and overlap syndrome

Yanan Sun; Zixian Wang; Yichen Gu; Binbin Wu; Shuhui Xie; Jing Wu

doi:10.12449/JCH260816

Volume 42 Issue 8

Aug. 2026

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Article Navigation > Journal of Clinical Hepatology > 2026 > 42(8): 1838-1844

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Value of two-dimensional shear wave elastography combined with serological markers in the differential diagnosis of primary biliary cholangitis and overlap syndrome

DOI: 10.12449/JCH260816

Yanan Sun^{1, 2},
Zixian Wang^{1, 2},
Yichen Gu^{1, 2},
Binbin Wu²,
Shuhui Xie²,
Jing Wu^{2
,
,
,}

1.
Medical School of Nantong University，Nantong，Jiangsu 226001，China
2.
Department of Ultrasound，Nantong Third Hospital Affiliated to Nantong University （Nantong Third People’s Hospital），Nantong，Jiangsu 226006，China

Research funding:

Nantong Municipal Health Commission Research Project (MS2025049);

Nantong University Special Research Fund for Clinical Medicine (2025LY030)

More Information

Corresponding author: Wu Jing， ntsywj@163.com （ORCID： 0000-0002-4037-3712）
Received Date: 2026-02-11
Accepted Date: 2026-03-30
Published Date: 2026-08-25

Abstract

Abstract

Objective To construct a machine learning model combining two-dimensional shear wave elastography （2D-SWE） and serological markers， and to investigate its clinical value in differentiating primary biliary cholangitis （PBC） from overlap syndrome （OS）. Methods A total of 199 patients with pathologically confirmed PBC or OS in Nantong Third People’s Hospital from January 2021 to December 2025 were retrospectively enrolled， with 125 patients in the PBC group and 74 in the OS group. The patients were randomly divided into a training set with 139 patients and a test set with 60 patients at a ratio of 7∶3. Related data were collected， including serological markers， conventional two-dimensional ultrasound parameters， and 2D-SWE parameters （including velocity of shear wave ［VS］ and liver fibrosis index ［LFI］）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The univariate and multivariate Logistic regression analyses were used to identify independent predictive factors， which were then incorporated into six machine learning models， and 5-fold cross-validation was used for optimization of parameters. The indicators including the area under the receiver operating characteristic curve （AUC） were compared in the test set to determine the best model， and the SHapley additive exPlanations （SHAP） analysis was used for model interpretation. Results Female patients accounted for 87.8% in the OS group and 79.2% in the PBC group. Compared with the PBC group， the OS group had significantly higher age， VS， LFI， splenic area， aspartate aminotransferase， total bilirubin， prothrombin time， immunoglobulin G （IgG）， and immunoglobulin M （Z=-2.883， -6.524， -4.000， -3.061， -2.194， -2.372， -4.079， -6.964， and -2.709， all P<0.05） and a significantly lower platelet count （Z=4.098， P<0.001）， and there were also significant differences between the two groups in the proportion of patients with positive anti-nuclear antibody， fibrosis stage， and inflammation grade （χ²=3.458， 63.198， and 101.038， all P<0.05）. Variables without multicollinearity were included in the regression analysis， and the multivariate Logistic regression analysis showed that VS （odds ratio ［OR］=4.503， 95% confidence interval ［CI］： 1.698 — 11.943， P=0.003）， LFI （OR=1.813， 95%CI： 1.050 — 3.132， P=0.033）， and IgG （OR=1.121， 95%CI： 1.026 — 1.226， P=0.012） were independent predictive factors for differentiating PBC from OS. Six machine learning models were constructed based on these variables， among which the logistic regression model showed the best predictive performance in the test set， with an AUC of 0.881 （95%CI： 0.788 — 0.974）， a sensitivity of 0.731， and a specificity of 0.794. The SHAP analysis showed that IgG contributed the most to model prediction. Conclusion The noninvasive multimodal machine learning model combining 2D-SWE parameters （VS， LFI） and serum IgG can effectively differentiate PBC from OS， providing a reference for clinical decision-making regarding the need for liver biopsy.
- Primary Biliary Cholangitis,
- Overlap Syndrome,
- Machine Learning,
- Elasticity Imaging Techniques,
- Immunoglobulin G

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References

[1]	European Association for the Study of the Liver. EASL Clinical Practice Guidelines: The diagnosis and management of patients with primary biliary cholangitis[J]. J Hepatol, 2017, 67( 1): 145- 172. DOI: 10.1016/j.jhep.2017.03.022.
[2]	European Association for the Study of the Liver. EASL Clinical Practice Guidelines on the management of autoimmune hepatitis[J]. J Hepatol, 2025, 83( 2): 453- 501. DOI: 10.1016/j.jhep.2025.03.017.
[3]	Efe C, Ozaslan E, Heurgué-Berlot A, et al. Sequential presentation of primary biliary cirrhosis and autoimmune hepatitis[J]. Eur J Gastroenterol Hepatol, 2014, 26( 5): 532- 537. DOI: 10.1097/MEG.0000000000000075.
[4]	Yang F, Wang Q X, Wang Z Y, et al. The natural history and prognosis of primary biliary cirrhosis with clinical features of autoimmune hepatitis[J]. Clin Rev Allergy Immunol, 2016, 50( 1): 114- 123. DOI: 10.1007/s12016-015-8516-5.
[5]	Park Y, Cho Y, Cho E J, et al. Retrospective analysis of autoimmune hepatitis-primary biliary cirrhosis overlap syndrome in Korea: Characteristics, treatments, and outcomes[J]. Clin Mol Hepatol, 2015, 21( 2): 150- 157. DOI: 10.3350/cmh.2015.21.2.150.
[6]	European Association for the Study of the Liver. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis-2021 update[J]. J Hepatol, 2021, 75( 3): 659- 689. DOI: 10.1016/j.jhep.2021.05.025.
[7]	Ferraioli G, Barr R G, Berzigotti A, et al. WFUMB guidelines/guidance on liver multiparametric ultrasound. Part 2: Guidance on liver fat quantification[J]. Ultrasound Med Biol, 2024, 50( 8): 1088- 1098. DOI: 10.1016/j.ultrasmedbio.2024.03.014.
[8]	Asif S, Yi W H, ur-Rehman S, et al. Advancements and prospects of machine learning in medical diagnostics: Unveiling the future of diagnostic precision[J]. Arch Comput Meth Eng, 2025, 32( 2): 853- 883. DOI: 10.1007/s11831-024-10148-w.
[9]	Wang W, Zhang J C, Tian W S, et al. Shear wave elastography-based ultrasomics: Differentiating malignant from benign focal liver lesions[J]. Abdom Radiol(NY), 2021, 46( 1): 237- 248. DOI: 10.1007/s00261-020-02614-3.
[10]	Dong B T, He R L, Ju S H, et al. Fibrosis-4plus score: A novel machine learning-based tool for screening high-risk varices in compensated cirrhosis(CHESS2004): An international multicenter study[J]. Clin Mol Hepatol, 2025, 31( 3): 881- 898. DOI: 10.3350/cmh.2024.0898.
[11]	Chinese Society of Ultrasound in Medicine; Oncology Intervention Committee of Chinese Research Hospital Society; National Health Commission Capacity Building and Continuing Education Expert Committee on Ultrasonic Diagnosis. Guideline for ultrasonic diagnosis of liver diseases[J]. J Clin Hepatol, 2021, 37( 8): 1770- 1785. DOI: 10.3969/j.issn.1001-5256.2021.08.007. 中华医学会超声医学分会, 中国研究型医院学会肿瘤介入专业委员会, 国家卫生健康委员会能力建设和继续教育中心超声医学专家委员会. 肝病超声诊断指南[J]. 临床肝胆病杂志, 2021, 37( 8): 1770- 1785. DOI: 10.3969/j.issn.1001-5256.2021.08.007.
[12]	Koga T, Morikawa Y. Ultrasonographic determination of the splenic size and its clinical usefulness in various liver diseases[J]. Radiology, 1975, 115( 1): 157- 161. DOI: 10.1148/115.1.157.
[13]	Scheuer P J. Classification of chronic viral hepatitis: A need for reassessment[J]. J Hepatol, 1991, 13( 3): 372- 374. DOI: 10.1016/0168-8278(91)90084-o.
[14]	Yan Y L, Xing X, Wang Y, et al. Clinical utility of two-dimensional shear-wave elastography in monitoring disease course in autoimmune hepatitis-primary biliary cholangitis overlap syndrome[J]. World J Gastroenterol, 2022, 28( 18): 2021- 2033. DOI: 10.3748/wjg.v28.i18.2021.
[15]	Muratori P, Granito A, Pappas G, et al. The serological profile of the autoimmune hepatitis/primary biliary cirrhosis overlap syndrome[J]. Am J Gastroenterol, 2009, 104( 6): 1420- 1425. DOI: 10.1038/ajg.2009.126.
[16]	Schulz L, Sebode M, Weidemann S A, et al. Variant syndromes of primary biliary cholangitis[J]. Best Pract Res Clin Gastroenterol, 2018, 34: 55- 61. DOI: 10.1016/j.bpg.2018.06.003.
[17]	Liang Y X, Khandakar B, Hao Y S, et al. Histology and clinical correlations in autoimmune hepatitis, primary biliary cholangitis, and autoimmune hepatitis-primary biliary cholangitis overlap syndrome[J]. Ann Diagn Pathol, 2023, 67: 152178. DOI: 10.1016/j.anndiagpath.2023.152178.
[18]	Yan Y L, Xing X, Lu Q, et al. Assessment of biopsy proven liver fibrosis by two-dimensional shear wave elastography in patients with primary biliary cholangitis[J]. Dig Liver Dis, 2020, 52( 5): 555- 560. DOI: 10.1016/j.dld.2020.02.002.
[19]	Chen Rui, Zheng Yijun, Gao Zhiling, et al. Value of ultrasound combined with elastography in evaluating inflammation grading in patients with chronic liver disease at S2 stage of liver fibrosis[J]. Acad J Nav Med Univ, 2025, 46( 7): 863- 868. DOI: 10.16781/j.CN31-2187/R.20240558. 陈蕊, 郑一君, 高志玲, 等. 超声联合弹性成像技术评估慢性肝病肝纤维化S2期患者炎症分级的价值[J]. 海军军医大学学报, 2025, 46( 7): 863- 868. DOI: 10.16781/j.CN31-2187/R.20240558.
[20]	Wang Y, Li Y N, Wang X Y, et al. Predicting liver disease risk using a combination of common clinical markers: A screening model from routine health check-up[J]. Dis Markers, 2020, 2020: 8460883. DOI: 10.1155/2020/8460883.
[21]	Wang K L, Li Y, Pan J F, et al. Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models[J]. Open Med(Wars), 2022, 17( 1): 1550- 1558. DOI: 10.1515/med-2022-0526.

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Value of two-dimensional shear wave elastography combined with serological markers in the differential diagnosis of primary biliary cholangitis and overlap syndrome

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