Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects.
Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects.
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DOI：
10.2147/ndt.s354265
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发表时间：
2022
影响因子：
3.2
通讯作者：
Murai, Toshiya
中科院分区：
文献类型：
--
作者：
Abdelrahman, Hiba Abuelgasim Fadlelmoula;Ubukata, Shiho;Ueda, Keita;Fujimoto, Gaku;Oishi, Naoya;Aso, Toshihiko;Murai, Toshiya
弥漫性轴索损伤（DAI）是创伤性脑损伤（TBI）最常见的病理特征之一。扩散张量成像（DTI）指数可用于识别和量化DAI后的白色物质微结构变化。最近，许多研究已经使用DTI与各种机器学习方法来预测TBI后的白色微结构变化。目前的研究试图检查我们使用多个DTI指数结合机器学习的分类方法是否是诊断/分类TBI患者和健康对照的有用工具。参与者是患有慢性TBI的成年患者（n = 26），具有DAI病理学，以及年龄和性别匹配的健康对照组（n = 26）。所有参与者均获得DTI图像。对DTI图像进行基于道的空间统计分析。利用主成分分析和支持向量机建立分类模型。使用受试者操作特征曲线分析和曲线下面积来评估不同分类器的分类性能。与健康对照组相比，TBI患者基于区域的空间统计显示各向异性分数显著降低，平均扩散率、轴向扩散率和径向扩散率增加（所有p值< 0.01）。主成分分析和基于支持向量机的机器学习分类使用组合DTI指数对TBI患者和健康对照进行分类，准确率为90.5%，曲线下面积为93 ± 0.09。这些结果突出了我们的方法结合多种DTI措施来识别TBI患者的潜力。
Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls. Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers. Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09. These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.