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    • Voxel- and Tensor-Based Morphometry with Machine Learning Techniques Identifying Characteristic Brain Impairment in Patients with Cervical Spondylotic Myelopathy

    Yang Wang,Rui Zhao,Dan Zhu,Xiuwei Fu, Fengyu Sun,Yuezeng Cai,Juanwei Ma,Xing Guo,Jing Zhang,Yuan Xue

    European neurology（2024）SCI 4区

    Tianjin Med Univ Gen Hosp

    Cited 1|Views30
    Abstract
    AimThe diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques.MethodsIn this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM.ResultsCSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P < 0.05, false discovery rate (FDR) corrected]. A multivariate pattern classification analysis revealed that VBM and TBM could successfully identify CSM patients and HCs [classification accuracy: 81.58%, area under the curve (AUC): 0.85; P < 0.005, Bonferroni corrected] through characteristic gray matter and white matter impairments.ConclusionCSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM.
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    cervical spondylotic myelopathy,structural MRI,tensor-based morphometry,voxel-based morphometry,multivariate pattern analysis
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    【要点】：本研究通过结合体素形态学（VBM）和张力形态学（TBM）以及机器学习技术，识别颈椎病性脊髓病（CSM）患者的大脑特征性损伤，提高了CSM的精确诊断。
    

    【方法】：研究使用支持向量机（SVM）对57名CSM患者和57名健康对照的灰质和白质结构特征进行分类。
    

    【实验】：通过回顾性研究，对患者的灰质和白质体积变化进行VBM和TBM分析，结果显示CSM患者在多个大脑区域表现出特征性结构异常，VBM和TBM结合的准确分类达到81.58%，曲线下面积（AUC）为0.85。
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