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

doi:10.11952/j.issn.1007-1954.2024.01.003

科研成果详情

题名	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
其他题名	Prediction of microvascular invasion of mass-forming intrahepatic cholangiocarcinoma based on contrast-enhanced CT radiomics and machine learning
作者	吕昊阳1; 洪重2; 黄侠鸣 1; 俞富祥1
发表日期	2024-01-15
发表期刊	肝胆胰外科杂志影响因子和分区
语种	中文
原始文献类型	学术期刊
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
其他关键词	intrahepatic cholangiocarcinoma ; microvascular invasion ; radiomics ; prediction model ; machine learning
摘要	目的研究肿块型肝内胆管癌发生微血管侵犯的影像组学及临床特征，并建立基于机器学习算法的预测模型。方法回顾性收集2015年1月至2023年2月期间就诊于温州医科大学附属第一医院（75例）和温州医科大学附属第二医院（29例）的肝内胆管癌（ICC）患者资料，提取增强CT的影像组学特征，使用多种机器学习方法进行分析并比较，结合最佳影像组学机器学习方法与临床资料，建立预测模型并进行检验。结果多种影像组学机器学习方法中，门脉期影像组学特征的朴素贝叶斯分类表现相对较好，曲线下面积（AUC）为0.818，结合筛选出的2个临床特征（瘤内动脉穿行，CEA>5 ng/mL）建立预测模型，训练组和测试组的AUC分别为0.883和0.891，训练组的敏感度为0.978，特异度为0.656，测试组的敏感度为0.909，特异度为0.700。结论基于增强CT影像组学机器学习结合临床资料的模型可用于预测肝内胆管癌的微血管侵犯状态，具有较好的诊断价值。
其他摘要	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.
资助项目	浙江省医药卫生科技计划项目（2019KY104）;
ISSN	1007-1954
卷号	36 期号:01 页码:13-19+25
DOI	10.11952/j.issn.1007-1954.2024.01.003
页数	8
收录类别	CNKI ; 万方 ; 维普
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引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/206086
专题	附属第一医院附属第二医院第二临床医学院、附属第二医院、育英儿童医院附属第二医院_外科
通讯作者	俞富祥
作者单位	1.温州医科大学附属第一医院肝胆胰外科; 2.温州医科大学附属第二医院肝胆胰外科
第一作者单位	附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	吕昊阳,洪重,黄侠鸣,等. 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(01):13-19+25.
APA	吕昊阳, 洪重, 黄侠鸣, & 俞富祥. (2024). 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. 肝胆胰外科杂志, 36(01), 13-19+25.
MLA	吕昊阳,et al."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".肝胆胰外科杂志 36.01(2024):13-19+25.