新发传染病电子杂志2025，Vol.10Issue(4)：58-64,7.DOI:10.19871/j.cnki.xfcrbzz.2025.04.010
基于CT图像的人体脂肪成分及影像组学在肝硬化不良结局预测中的应用
Application of CT-based body adipose composition and radiomics in predicting adverse outcomes in patients with cirrhosis
谢囡霭 1梁译文 1罗梓欣 1邹观南 1郭峰 2蒋奕1
作者信息
- 1. 香港中文大学(深圳)附属第二医院/深圳市龙岗区人民医院影像科,广东 深圳 518172
- 2. 新疆维吾尔自治区中医医院肝病科,新疆 乌鲁木齐 830000
- 折叠
摘要
Abstract
Objective This study aimed to explore the predictive efficacy of body adipose composition analysis using CT imaging for adverse outcomes in patients with cirrhosis and to enhance predictive performance by integrating a radiomics model based on liver and spleen regions.Method 243 patients with liver cirrhosis were retrospectively enrolled from two centers between September 2016 and December 2020,including 153 cases of hepatitis B or C virus-related cirrhosis.Patients were classified into non-adverse outcome group(n=119)and adverse outcome group(n=124)based on the occurrence of severe complications such as spontaneous bacterial peritonitis during the follow-up period(over 3 months).Quantitative measurements of body adipose composition were performed at the third lumbar vertebra level,and radiomic features of the liver and spleen were extracted.Multiple machine learning models were developed to evaluate predictive performance.Result The area under the receiver operating characteristic curve(AUC)for the adipose tissue model at the two centers was 0.804 and 0.749,respectively,outperforming the radiomics model(AUC=0.767 and 0.712).Shapley additive explanations(SHAP)analysis identified intermuscular fat ratio and subcutaneous fat volume as key predictors of adverse outcomes.The predictive efficacy of the combined model was significantly improved(AUC=0.856 and 0.780;integrated discrimination improvement index=0.125,0.163,P＜0.05).Conclusion body adipose composition can effectively predict the risk of adverse outcomes in patients with viral hepatitis-related cirrhosis.The integration of liver and spleen radiomics features further enhances predictive accuracy,offering a promising approach for individualized risk management and early intervention in viral liver diseases.关键词
肝硬化/预后预测/计算机断层扫描/脂肪组织/机器学习Key words
Liver cirrhosis/Prognostic prediction/Computed tomography/Adipose Tissue/Machine learning分类
医药卫生引用本文复制引用
谢囡霭,梁译文,罗梓欣,邹观南,郭峰,蒋奕..基于CT图像的人体脂肪成分及影像组学在肝硬化不良结局预测中的应用[J].新发传染病电子杂志,2025,10(4):58-64,7.
