Identification of high-risk carotid plaque with MRI-based radiomics and machine learning
Identification of high-risk carotid plaque with MRI-based radiomics and machine learning
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DOI：
10.1007/s00330-020-07361-z
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发表时间：
2020-10-17
影响因子：
5.9
通讯作者：
Lin, Jiang
中科院分区：
文献类型：
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作者：
Zhang, Ranying;Zhang, Qingwei;Lin, Jiang
我们试图建立一个基于高危斑块mri的模型（HRPMM），利用放射组学特征和机器学习来区分有症状和无症状的颈动脉斑块。材料与方法将162例颈动脉狭窄患者随机分为训练组和试验组。进行飞行时间（TOF）、T1、t2加权成像及增强成像。记录并计算颈动脉斑块的放射学特征，建立传统模型。在提取这些图像的放射组学特征后，我们在训练队列中构建了最小绝对收缩和选择算子算法的HRPMM，并对其在测试队列中的表现进行了评估。利用传统和放射组学特征建立了一个组合模型。比较各模型对高危颈动脉斑块的识别性能。结果斑块内出血和富含脂质坏死核心与临床症状独立相关，用于建立传统模型，其曲线下面积（AUC）为0.825，而训练组和试验组的AUC为0.804。在两个队列中，HRPMM和联合模型的AUC分别为0.988对0.984和0.989对0.986。放射组学模型和联合模型均优于传统模型，而联合模型与HRPMM无显著差异。结论基于mri的放射组学模型可以准确区分有症状和无症状的颈动脉斑块。在高危斑块的识别上优于传统模型。
Objectives We sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques. Materials and methods One hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared. Results Intraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM. Conclusions Our MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques.