使用基于mri的脑结构萎缩率的聚类方法对退行性小脑共济失调的广谱疾病进展进行分层。

Q3 Medicine
Cerebellum and Ataxias Pub Date : 2017-06-29 eCollection Date: 2017-01-01 DOI:10.1186/s40673-017-0068-4
Rie Sasaki, Futaba Maki, Daisuke Hara, Shigeaki Tanaka, Yasuhiro Hasegawa
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引用次数: 4

摘要

背景:退行性小脑共济失调患者的疾病进展率不同。个体患者不确定的自然病程阻碍了有希望的治疗方法的临床试验。在这项研究中,我们用聚类分析分析了脑结构的萎缩变化,以发现在广谱退行性小脑共济失调中具有同质症状进展的患者亚组。方法48例患者,其中脊髓小脑性共济失调(SCA) 21例,小脑型多系统萎缩(MSA-C) 17例,皮质性小脑性共济失调(CCA) 10例。在所有患者中,至少进行两组评估,包括磁共振成像(MRI)和国际合作共济失调评定量表(ICARS)评分。每位患者随访研究的中位数(min-max)为3个(2-6个),平均随访时间为3.1±1.6年。使用MRI测量中矢状面图像上胼胝体的面积和小脑体积,并将这些值除以每个患者的颅前后直径,分别作为面积指数(Adx)和体积指数(Vdx)来纠正个体头部大小的差异。计算每位患者的Adx、Vdx、ICARS评分的年度变化,并对患者的萎缩模式进行聚类分析。结果:不同亚型小脑共济失调患者胼胝体(Adx)和小脑(Vdx)的年萎缩率及症状进展差异有统计学意义(p分别为0.026、0.019和0.021)。然而,Adx和Vdx的年萎缩率与ICARS评分的年增长均无显著相关。将患者按Adx和Vdx的年变化情况分为3组时,各组间ICARS评分的年增长差异有统计学意义(第1组为2.9±1.7/年,第2组为4.8±3.2/年,第3组为8.7±6.1/年);p = 0.014)。结论:基于胼胝体和小脑萎缩率的聚类分析可以对ICARS评分的年增长进行分层。这些简单的MRI方法是否可以在临床试验中用于广泛的退行性小脑共济失调患者的随机分配,需要进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stratification of disease progression in a broad spectrum of degenerative cerebellar ataxias with a clustering method using MRI-based atrophy rates of brain structures.

Stratification of disease progression in a broad spectrum of degenerative cerebellar ataxias with a clustering method using MRI-based atrophy rates of brain structures.

Stratification of disease progression in a broad spectrum of degenerative cerebellar ataxias with a clustering method using MRI-based atrophy rates of brain structures.

Stratification of disease progression in a broad spectrum of degenerative cerebellar ataxias with a clustering method using MRI-based atrophy rates of brain structures.

Background: The rate of disease progression differs among patients with degenerative cerebellar ataxia. The uncertain natural course in individual patients hinders clinical trials of promising treatments. In this study, we analyzed atrophy changes in brain structures with cluster analysis to find sub-groups of patients with homogenous symptom progression in a broad spectrum of degenerative cerebellar ataxias.

Methods: We examined 48 patients including 21 cases of spinocerebellar ataxia (SCA), 17 cases of the cerebellar type of multiple system atrophy (MSA-C), and 10 cases of cortical cerebellar ataxia (CCA). In all patients, at least two sets of evaluations including magnetic resonance imaging (MRI) and the International Cooperative Ataxia Rating Scale (ICARS) scoring were performed. The median number (min-max) of follow-up studies in each patient was three (2-6), and the mean follow-up period was 3.1 ± 1.6 years. The area of the corpus callosum on midsagittal images and the cerebellar volume were measured using MRI, and these values were divided by the cranial antero-posterior diameter of each patient to correct for individual head size differences as an area index (Adx) and a volume index (Vdx), respectively. The annual changes in Adx, Vdx, and ICARS score were calculated in each patient, and atrophy patterns in patients were categorized with cluster analysis.

Results: The annual atrophy rates for the corpus callosum (Adx) and cerebellum (Vdx) and symptom progression differed significantly by subtype of cerebellar ataxia (p = 0.026, 0.019, and 0.021, respectively). However, neither the annual atrophy rate of Adx nor Vdx was significantly correlated with the annual increase in the ICARS score. When the patients were categorized into three clusters based on the annual changes in Adx and Vdx, the annual increase in the ICARS score was significantly different among clusters (2.9 ± 1.7/year in Cluster 1, 4.8 ± 3.2/year in Cluster 2, and 8.7 ± 6.1/year in Cluster 3; p = 0.014).

Conclusions: The annual increase in the ICARS score can be stratified by cluster analysis based on the atrophy rates of the corpus callosum and cerebellum. Further studies are warranted to explore whether these simple MRI methods could be used for random allocation of a broad spectrum of patients with degenerative cerebellar ataxia in clinical trials.

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Cerebellum and Ataxias
Cerebellum and Ataxias Medicine-Neurology (clinical)
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