肝胆胰外科杂志2024，Vol.36Issue(1)：13-19,25,8.DOI:10.11952/j.issn.1007-1954.2024.01.003
基于增强CT影像组学与机器学习算法预测肿块型肝内胆管癌微血管侵犯
Prediction of microvascular invasion of mass-forming intrahepatic cholangiocarcinoma based on contrast-enhanced CT radiomics and machine learning
摘要
Abstract
Objective To study the radiomics and clinical characteristics of intratumoral microvascular invasion in mass-forming intrahepatic cholangiocarcinoma(ICC)and establish a predictive model with the best machine learning algorithm.Methods We retrospectively collected the data from 75 patients with ICC who were treated at the First Affiliated Hospital of Wenzhou Medical University and 29 patients with ICC who were treated at the Second Affiliated Hospital of Wenzhou Medical University between Jan.2015 and Feb.2023.Radiomic features were extracted from contrast-enhanced CT images,and various machine learning methods were used for analysis.The best radiomics machine learning method was combined with clinical data to establish a predictive model,which was then validated.Results Among various radiomic machine learning methods,naive Bayes classification based on portal venous phase imaging features performed relatively well,the area under the cure(AUC)was 0.818.By combining two selected clinical features(tumor arterial penetration,CEA＞5 ng/mL),a predictive model was established with the AUC of 0.883 in the training group and 0.891 in the testing group.In the training group,sensitivity was 0.978,while specificity was 0.656,and in the testing group,sensitivity was 0.909,while specificity was 0.700.Conclusion The model based on radiomic machine learning combined with clinical data from contrast-enhanced CT has good diagnostic value for predicting microvascular invasion status in ICC.关键词
肝内胆管癌/微血管侵犯/影像组学/预测模型/机器学习Key words
intrahepatic cholangiocarcinoma/microvascular invasion/radiomics/prediction model/machine learning分类
医药卫生引用本文复制引用
吕昊阳,洪重,黄侠鸣,俞富祥..基于增强CT影像组学与机器学习算法预测肿块型肝内胆管癌微血管侵犯[J].肝胆胰外科杂志,2024,36(1):13-19,25,8.基金项目
浙江省医药卫生科技计划项目(2019KY104). （2019KY104）
