首页> 外文期刊>AJNR. American journal of neuroradiology >Regional white matter atrophy--based classification of multiple sclerosis in cross-sectional and longitudinal data.
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Regional white matter atrophy--based classification of multiple sclerosis in cross-sectional and longitudinal data.

机译:基于区域白质萎缩的横断面和纵向数据中的多发性硬化分类。

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BACKGROUND AND PURPOSE: The different clinical subtypes of multiple sclerosis (MS) may reflect underlying differences in affected neuroanatomic regions. Our aim was to analyze the effectiveness of jointly using the inferior subolivary medulla oblongata volume (MOV) and the cross-sectional area of the corpus callosum in distinguishing patients with relapsing-remitting multiple sclerosis (RRMS), secondary-progressive multiple sclerosis (SPMS), and primary-progressive multiple sclerosis (PPMS). MATERIALS AND METHODS: We analyzed a cross-sectional dataset of 64 patients (30 RRMS, 14 SPMS, 20 PPMS) and a separate longitudinal dataset of 25 patients (114 MR imaging examinations). Twelve patients in the longitudinal dataset had converted from RRMS to SPMS. For all images, the MOV and corpus callosum were delineated manually and the corpus callosum was parcellated into 5 segments. Patients from the cross-sectional dataset were classified as RRMS, SPMS, or PPMS by using a decision tree algorithm with the following input features: brain parenchymal fraction, age, disease duration, MOV, total corpus callosum area and areas of 5 segments of the corpus callosum. To test the robustness of the classification technique, we applied the results derived from the cross-sectional analysis to the longitudinal dataset. RESULTS: MOV and central corpus callosum segment area were the 2 features retained by the decision tree. Patients with MOV >0.94 cm(3) were classified as having RRMS. Patients with progressive MS were further subclassified as having SPMS if the central corpus callosum segment area was <55.12 mm(2), and as having PPMS otherwise. In the cross-sectional dataset, 51/64 (80%) patients were correctly classified. For the longitudinal dataset, 88/114 (77%) patient time points were correctly classified as RRMS or SPMS. CONCLUSIONS: Classification techniques revealed differences in affected neuroanatomic regions in subtypes of multiple sclerosis. The combination of central corpus callosum segment area and MOV provides good discrimination among patients with RRMS, SPMS, and PPMS.
机译:背景与目的:多发性硬化症(MS)的不同临床亚型可能反映了受影响的神经解剖区域的潜在差异。我们的目的是分析联合使用下橄榄下延髓量(MOV)和call体截面积来区分复发缓解型多发性硬化症(RRMS),继发性进行性多发性硬化症(SPMS)的有效性,以及原发进行性多发性硬化症(PPMS)。材料与方法:我们分析了64例患者的横断面数据集(30个RRMS,14个SPMS,20个PPMS)和25个患者的单独纵向数据集(114例MR成像检查)。纵向数据集中的12位患者已从RRMS转换为SPMS。对于所有图像,手动绘制MOV和call体,并将call体分成5个部分。通过使用具有以下输入特征的决策树算法,将来自横截面数据集的患者分为RRMS,SPMS或PPMS:脑实质部分,年龄,疾病持续时间,MOV,总call体面积和5个部分的面积。胼胝体。为了测试分类技术的鲁棒性,我们将横截面分析得出的结果应用于纵向数据集。结果:MOV和中央体节区是决策树保留的两个特征。 MOV> 0.94 cm(3)的患者被分类为具有RRMS。如果中央体节段面积<55.12 mm(2),则进行性MS患者可进一步分为SPMS,否则分为PPMS。在横截面数据集中,正确分类了51/64(80%)患者。对于纵向数据集,将88/114(77%)个患者时间点正确分类为RRMS或SPMS。结论:分类技术揭示了多发性硬化亚型的受影响神经解剖区域的差异。中央体节段面积和MOV的结合为RRMS,SPMS和PPMS患者提供了良好的区分能力。

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