Scherfler Christoph, Göbel Georg, Müller Christoph, Nocker Michael, Wenning Gregor K, Schocke Michael, Poewe Werner, Seppi Klaus
From the Departments of Neurology (C.S., C.M., M.N., G.K.W., W.P., K.S.), Medical Statistics, Informatics and Health Economics (G.G.), and Radiology (M.S.), Medical University of Innsbruck, Austria.
Neurology. 2016 Mar 29;86(13):1242-9. doi: 10.1212/WNL.0000000000002518. Epub 2016 Mar 2.
To determine whether automated and observer-independent volumetric MRI analysis is able to discriminate among patients with Parkinson disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) in early to moderately advanced stages of disease.
T1-weighted volumetric MRI from patients with clinically probable PD (n = 40), MSA (n = 40), and PSP (n = 30) and a mean disease duration of 2.8 ± 1.7 y were examined using automated volume measures of 22 subcortical regions. The clinical follow-up period was 2.5 ± 1.2 years. The data were split into a training (n = 72) and a test set (n = 38). The training set was used to build a C4.5 decision tree model in order to classify patients as MSA, PSP, or PD. The classification algorithm was examined by the test set using the final clinical diagnosis at last follow-up as diagnostic gold standard.
The midbrain and putaminal volume as well as the cerebellar gray matter compartment were identified as the most significant brain regions to construct a prediction model. The diagnostic accuracy for PD vs MSA or PSP was 97.4%. In contrast, diagnostic accuracy based on validated clinical consensus criteria at the time of MRI acquisition was 62.9%.
Volume segmentation of subcortical brain areas differentiates PD from MSA and PSP and improves diagnostic accuracy in patients presenting with early to moderately advanced stage parkinsonism.
This study provides Class III evidence that automated MRI analysis accurately discriminates among early-stage PD, MSA, and PSP.
确定自动化且不依赖观察者的容积磁共振成像（MRI）分析能否在帕金森病（PD）、多系统萎缩（MSA）和进行性核上性麻痹（PSP）疾病的早至中度晚期阶段对患者进行区分。
对临床很可能为PD（n = 40）、MSA（n = 40）和PSP（n = 30）且平均病程为2.8±1.7年的患者进行T1加权容积MRI检查，使用22个皮质下区域的自动容积测量方法。临床随访期为2.5±1.2年。数据被分为训练集（n = 72）和测试集（n = 38）。训练集用于构建C4.5决策树模型，以便将患者分类为MSA、PSP或PD。测试集使用最后随访时的最终临床诊断作为诊断金标准来检验分类算法。
中脑和壳核体积以及小脑灰质区被确定为构建预测模型最重要的脑区。PD与MSA或PSP的诊断准确率为97.4%。相比之下，基于MRI采集时经验证的临床共识标准的诊断准确率为62.9%。
皮质下脑区的容积分割可将PD与MSA和PSP区分开来，并提高早至中度晚期帕金森综合征患者的诊断准确率。
本研究提供了III类证据，即自动化MRI分析能准确区分早期PD、MSA和PSP。
