Value of two-dimensional shear wave elastography combined with serological markers in the differential diagnosis of primary biliary cholangitis and overlap syndrome
- VernacularTitle:二维剪切波弹性成像联合血清学指标对原发性胆汁性胆管炎与重叠综合征的鉴别诊断价值
- Author:
Yanan SUN
1
;
Zixian WANG
1
;
Yichen GU
1
;
Binbin WU
2
;
Shuhui XIE
2
;
Jing WU
2
Author Information
- Publication Type:Journal Article
- Keywords: Primary Biliary Cholangitis; Overlap Syndrome; Machine Learning; Elasticity Imaging Techniques; Immunoglobulin G
- From: Journal of Clinical Hepatology 2026;42(8):1838-1844
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo 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）. MethodsA 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. ResultsFemale 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. ConclusionThe 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.
