目的 构建识别临床高风险颈动脉斑块的影像组学模型.方法 回顾性分析2016年12月—2022年6月中日友好医院颈动脉狭窄患者的临床资料.依据6个月内是否出现卒中、短暂性脑缺血发作及其他脑血管临床症状将患者分为临床高风险斑块组和临床低风险斑块组,纳入关键变量建立极致梯度提升、支持向量机、高斯朴素贝叶斯、逻辑回归、K最邻近以及人工神经网络6种机器学习模型,随后结合logistic回归分析临床危险因素构建联合预测模型.结果 最终纳入患者652例,其中男427例、女225例,平均年龄68.2岁.分析结果显示在6种影像组学机器学习模型中,极致梯度提升模型的表现最好,验证集曲线下面积(area under the curve,AUC)为0.751,同时利用临床资料以及颈动脉影像资料建立的极致梯度提升联合预测模型验证集AUC为0.823.结论 影像组学特征联合临床特征构建模型可以有效预识别临床高风险颈动脉斑块.
Construction of a machine learning model for identifying clinical high-risk carotid plaques based on radiomics
Objective To construct a radiomics model for identifying clinical high-risk carotid plaques.Methods A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022.The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke,transient ischemic attack and other cerebrovascular clinical symptoms within six months.Six machine learning models including eXtreme Gradient Boosting,support vector machine,Gaussian Naive Bayesian,logical regression,K-nearest neighbors and artificial neural network were established.We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors.Results Finally 652 patients were collected,including 427 males and 225 females,with an average age of 68.2 years.The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models,and the area under the curve(AUC)in validation dataset was 0.751.At the same time,the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823.Conclusion Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.
NETL
NSTL
万方数据
维普
