目的:Radiomics是一种能够分析可挖掘成像特征的高通量提取的定量方法。在这里,我们的目标是开发一种基于CT血管造影的颈动脉斑块影像组学分析和机器学习模型,以区分易损斑块和无易损斑块.
方法:本试验纳入30例颈动脉粥样硬化患者。在手术中,采用了斑块的二元分类(“硬”vs“软”)。使用R软件包Moddicom进行特征提取。使用Spearman等级相关系数评估了成对特征的相互依赖性。进行单变量分析以评估每个特征与斑块分类之间的关联,并选择排名最高的特征。使用二元逻辑回归研究特征预测值。执行逐步向后消除程序以最小化Akaike信息标准(AIC)。最后的显著特征用于建立颈动脉斑块的二元分类模型,包括逻辑回归(LR),支持向量机(SVM),以及分类和回归树分析(CART)。所有模型均使用五次交叉验证进行交叉验证。特定类别的准确性,精度,召回率和F测量评价指标用于量化分类器输出质量。
结果:从每个斑块中总共提取了230个影像组学特征。特征之间的成对Spearman相关性报告了高水平的相关性,在|ρ|>0.8处,80%以上与至少一个其他特征相关。在逐步向后消除程序之后,发现熵和体积特征与两个斑块组最显著相关(p<0.001),AUC分别为0.92和0.96。最佳性能是由具有RBF内核的SVM分类器注册的,准确地说,精度,召回率和F分数分别等于86.7、92.9、81.3和86.7%,分别。用于熵和体积特征模型的CART分类树模型实现了86.7%的良好分类的斑块和0.987的AUC。
结论:这项初步研究强调了基于CTA的影像组学和机器学习在区分斑块组成方面的潜力。这种新方法有可能提供一种可靠的方法来改善颈动脉粥样硬化患者的风险分层。
OBJECTIVE: Radiomics is a quantitative method able to analyze a high-throughput extraction of minable imaging features. Herein, we aim to develop a CT angiography-based radiomics analysis and machine learning model for carotid
plaques to discriminate vulnerable from no vulnerable
plaques.
METHODS: Thirty consecutive patients with carotid atherosclerosis were enrolled in this pilot
study. At surgery, a binary classification of
plaques was adopted (\"hard\" vs \"soft\"). Feature extraction was performed using the R software package Moddicom. Pairwise feature interdependencies were evaluated using the Spearman rank correlation coefficient. A univariate analysis was performed to assess the association between each feature and the plaque classification and chose top-ranked features. The feature predictive value was investigated using binary logistic regression. A stepwise backward elimination procedure was performed to minimize the Akaike information criterion (AIC). The final significant features were used to build the models for binary classification of carotid plaques, including logistic regression (LR), support vector machine (SVM), and classification and regression tree analysis (CART). All models were cross-validated using fivefold cross validation. Class-specific accuracy, precision, recall and F-measure evaluation metrics were used to quantify classifier output quality.
RESULTS: A total of 230 radiomics features were extracted from each plaque. Pairwise Spearman correlation between features reported a high level of correlations, with more than 80% correlating with at least one other feature at |ρ|> 0.8. After a stepwise backward elimination procedure, the entropy and volume features were found to be the most significantly associated with the two plaque groups (p < 0.001), with AUCs of 0.92 and 0.96, respectively. The best performance was registered by the SVM classifier with the RBF kernel, with accuracy, precision, recall and F-score equal to 86.7, 92.9, 81.3 and 86.7%, respectively. The CART classification tree model for the entropy and volume features model achieved 86.7% well-classified plaques and an AUC of 0.987.
CONCLUSIONS: This pilot
study highlighted the potential of CTA-based radiomics and machine learning to discriminate plaque composition. This new approach has the potential to provide a reliable method to improve risk stratification in patients with carotid atherosclerosis.