Portal venous phase CT radiomics model based on machine learning for predicting postoperative complications of hepatic alveolar echinococcosis
10.13929/j.issn.1003-3289.2025.09.017
- VernacularTitle:基于机器学习门静脉期CT影像组学模型预测肝泡型棘球蚴病术后并发症
- Author:
Yuan ZHANG
1
;
Juan HOU
;
Yucai ZHU
;
ABUDURESULI·TU'ERSUN
;
Hui GUO
Author Information
1. 新疆医科大学附属中医医院影像科,新疆乌鲁木齐 830000
- Publication Type:Journal Article
- Keywords:
echinococcosis,hepatic;
postoperative complications;
tomography,X-ray computed;
machine learning;
radiomics
- From:
Chinese Journal of Medical Imaging Technology
2025;41(9):1535-1539
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of portal venous phase CT radiomics model based on machine learning(ML)for predicting postoperative complications of hepatic alveolar echinococcosis(HAE).Methods Totally 265 HAE patients were retrospectively enrolled and randomly divided into training set(n=185,including 106 cases with postoperative complications)and validation set(n=80,including 40 cases with postoperative complications)at a ratio of 7∶3.Based on portal venous phase CT images,HAE lesions were segmented,and radiomics features were extracted and screened.Totally 5 ML algorithms were used to construct models,and their performance for predicting postoperative complications were compared.Results Among 5 ML radiomics models,support vector machine(SVM)model had the best overall performance for predicting postoperative complications of HAE in both training and validation sets.DeLong test showed that in training set,the area under the curve(AUC)of SVM model was significantly higher than that of logistic regression(LR),K-nearest neighbor(KNN)and multilayer perceptron(MLP)models(all P＜0.001),while in validation set,the AUC of SVM model was significantly higher than that of adaptive boosting(AdaBoost)model(P=0.007).Decision curve analysis indicated that SVM model had the highest clinical net benefit.Conclusion Portal venous phase CT radiomics model based on ML algorithms,especially SVM algorithm,could effectively predict postoperative complications of HAE.
