PT - JOURNAL文章AU - Arman Eshaghi AU - Viktor Wottschel AU - Rosa Cortese AU - Massimiliano Calabrese AU - Mohammad Ali Sahraian AU - Alan J. Thompson AU - Daniel C. Alexander AU - Olga Ciccarelli TI -使用随机森林AID - 10.1212/WNL.0000000000003395进行视神经脊髓炎与多发性硬化症的灰质MRI鉴别DP - 2016年12月TA - Neur首页ology PG - 2463—2470 VI - 87 IP - 23 4099 - //www.ez-admanager.com/content/87/23/2463.short 4100 - //www.ez-admanager.com/content/87/23/2463.full SO - Neurology2016年12月6日;目的:我们测试脑灰质(GM)成像测量是否可以使用随机森林分类来区分多发性硬化症(MS)和视神经脊髓炎(NMO)。方法:在伊朗德黑兰研究了90名参与者(25名MS患者，30名NMO患者和35名健康对照[hc])，在意大利帕多瓦研究了54名参与者(24名MS患者，20名NMO患者和10名hc)。参与者接受脑T1和T2/液体衰减反转恢复MRI检查。计算50个皮质GM区域的体积、厚度和表面积以及GM深部核的体积，并构建3个随机森林模型，将患者分为NMO或MS，并将每组患者与hc分开。临床诊断是计算准确性的金标准。结果:该分类器将萎缩更严重的MS患者(尤其是GM深部)与NMO患者区分开来，平均准确率为74%(敏感性/特异性:77/72;p & lt;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

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