TY - T1的灰质核磁共振区分neuromyelitis视使用随机森林从多发性硬化症JF -神经学乔-神经病学SP - 2463 LP - 2470 - 10.1212 / WNL。首页0000000000003395六世Arman Eshaghi - 87 - 23盟盟-维克多Wottschel盟罗莎Cortese AU -诺阿花茎甘蓝AU -默罕默德·阿里Sahraian盟-艾伦·j·汤普森AU -丹尼尔·c·亚历山大盟奥尔加Ciccarelli Y1 - 2016/12/06 UR - //www.ez-admanager.com/content/87/23/2463.abstract N2 -目的:我们测试大脑灰质(GM)成像技术措施是否能区分多发性硬化症(M首页S)和视neuromyelitis(动)使用random-forest分类。方法:九十名参与者(25 MS患者,30动,患者和35名健康对照组[高碳钢])研究了在德黑兰,伊朗,和54 (24 MS患者,20例正常时差,和10高碳钢)在帕多瓦,意大利。参与者接受了脑部MRI T1和T2 / fluid-attenuated反转恢复。体积、厚度和表面50皮质通用区域和卷的通用计算核和用于构建3 random-forest模型将患者分类为动或女士,和每个病人组和高碳钢分开。临床诊断的金标准的精度计算。结果:患者分类器杰出女士,显示更大的萎缩尤其是通用汽车,从那些动平均准确率为74%(敏感性和特异性:77/72;p < 0.01). When we used thalamic volume (the most discriminating GM measure) together with the white matter lesion volume, the accuracy of the classification of MS vs NMO was 80%. The classifications of MS vs HCs and NMO vs HCs achieved higher accuracies (92% and 88%).Conclusions: GM imaging biomarkers, automatically obtained from clinical scans, can be used to distinguish NMO from MS, even in a 2-center setting, and may facilitate the differential diagnosis in clinical practice.Classification of evidence: This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS.AQP4-Ab=aquaporin-4 autoantibody; EDSS=Expanded Disability Status Scale; FLAIR=fluid-attenuated inversion recovery; GM=gray matter; HC=healthy control; LPBA=LONI Probabilistic Brain Atlas; MS=multiple sclerosis; NMO=neuromyelitis optica; NMOSD=neuromyelitis optica spectrum disorder ER -