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基于表面的多系统萎缩神经影像学模式。

Surface-Based Neuroimaging Pattern of Multiple System Atrophy.

机构信息

Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.).

Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.).

出版信息

Acad Radiol. 2023 Dec;30(12):2999-3009. doi: 10.1016/j.acra.2023.04.014. Epub 2023 Jul 24.

Abstract

RATIONALE AND OBJECTIVES

Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect.

MATERIALS AND METHODS

We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model.

RESULTS

Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%.

CONCLUSION

The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.

摘要

原理和目的

重叠的帕金森病、小脑性共济失调和锥体束征使多系统萎缩(MSA)的临床诊断具有挑战性。神经影像学模式有助于了解其病理生理学并提高其诊断效果。

材料和方法

我们回顾性地获取了 MSA(包括帕金森型[MSA-P]和小脑型[MSA-C])、帕金森病和正常对照组患者的磁共振成像和磁敏感加权成像。我们量化了神经影像学特征,以确定诊断的最佳阈值。此外,我们通过对皮质下形态改变进行映射和构建诊断模型来探索 MSA 的神经影像学模式。

结果

与对照组相比,MSA-P 患者的标准化壳核体积明显降低(P<.001),MSA-C 患者的标准化脑桥体积明显降低(P<.001)。基于阈值的临床预测模型的 Youden 指数在 MSA 患者中为 0.871-0.928。MSA 患者的神经影像学模式共同位于外侧壳核,神经影像学模式预测模型的分类准确率为 83.9%-100%。

结论

定量神经影像学特征和基于表面的形态异常代表了 MSA 的标志物,为个性化临床诊断开辟了新途径。

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