Prediction model of portal pressure for HBV-related cirrhosis based on radiomics feature
10.3760/cma.j.cn431274-20220419-00360
- VernacularTitle:基于图像人工分割建立乙肝肝硬化门脉压力预测模型
- Author:
Qintian TAN
1
;
Kun ZHOU
;
Yingjie AI
;
Siyu JIANG
;
Zekuan YU
;
Ling WU
;
Xiaoquan HUANG
;
Shiyao CHEN
Author Information
1. 复旦大学附属中山医院消化科，上海 200032
- Keywords:
Hepatitis B;
Liver cirrhosis;
Portal pressure;
Hepatic venous pressure gradient;
Prediction model
- From:
Journal of Chinese Physician
2022;24(5):658-661,666
- CountryChina
- Language:Chinese
-
Abstract:
Objective:In this study, the liver, spleen, and hepatic portal vein in the portal venous phase images of abdominal enhanced computed tomography (CT) are artificially segmented and annotated, and the radiomics features are extracted from them. A model for predicting portal pressure in patients with hepatitis B virus (HBV) related cirrhosis is constructed by combining radiomics features with clinical indicators.Methods:A total of 171 patients who had abdominal enhancement CT examination and trans-jugular hepatic venous pressure gradient (HVPG) measurement at the same time were enrolled from January 2016 to May 2020 in the Zhongshan Hospital Affiliated to Fudan University. The liver, spleen, and hepatic portal vein in the portal venous phase images of the CT were manually labeled by using ITK-SNAP 3.8 software. The radiomics features of these three sites were extracted using Python programming, and an HVPG prediction model was established.Results:A total of 171 patients was included in the study. The average age was (51.1±10.3)years, of which 134(78.4%) were males, and the average HVPG was 16.87±5.695. A total of 2 553 radiomics features were extracted from three sites of the portal venous phase images of abdominal enhanced CT in each patient. The 2 553 features extracted were screened using LASSO, and by combing with clinical features and radiomics features, the predictive model of HVPG was obtained: m_HVPG=31.622+ 0.028 8T×total bile acids-6.31(portal venous wavelet-LHH_glcm_ClusterShade)=0.253(portal venous wavelet-LHL_glszm_LargeAreaLowGrayLevelEmphasis)-20.9(spleen wavelet-LLH_glcm_Correlation)-0.000 127(liver original_shape_SurfaceArea)+ 2.79(liver wavelet-LLH_glcm_ClusterShade). The coefficient of determination R2 was 0.345. Conclusions:The study suggests that radiomics features of the liver, spleen, and portal venous combined with clinical features may be used as a non-invasive method to assess the portal pressure in patients with HBV-related cirrhosis.
