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用于诊断以帕金森症状为主的多系统萎缩的半自动算法

Semiautomated Algorithm for the Diagnosis of Multiple System Atrophy With Predominant Parkinsonism.

作者信息

Lee Woong-Woo, Kim Han-Joon, Lee Hong Ji, Kim Han Byul, Park Kwang Suk, Sohn Chul-Ho, Jeon Beomseok

机构信息

Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.

Department of Neurology, Eulji University College of Medicine, Daejeon, Korea.

出版信息

J Mov Disord. 2022 Sep;15(3):232-240. doi: 10.14802/jmd.21178. Epub 2022 Jul 26.

Abstract

OBJECTIVE

Putaminal iron deposition is an important feature that helps differentiate multiple system atrophy with predominant parkinsonism (MSA-p) from Parkinson's disease (PD). Most previous studies used visual inspection or quantitative methods with manual manipulation to perform this differentiation. We investigated the value of a new semiautomated diagnostic algorithm using 3T-MR susceptibility-weighted imaging for MSA-p.

METHODS

This study included 26 MSA-p, 68 PD, and 41 normal control (NC) subjects. The algorithm was developed in 2 steps: 1) determine the image containing the remarkable putaminal margin and 2) calculate the phase-shift values, which reflect the iron concentration. The next step was to identify the best differentiating conditions among several combinations. The highest phaseshift value of each subject was used to assess the most effective diagnostic set.

RESULTS

The raw phase-shift values were present along the lateral margin of the putamen in each group. It demonstrates an anterior- to-posterior gradient that was identified most frequently in MSA-p. The average of anterior 5 phase shift values were used for normalization. The highest area under the receiver operating characteristic curve (0.874, 80.8% sensitivity, and 86.7% specificity) of MSA-p versus PD was obtained under the combination of 3 or 4 vertical pixels and one dominant side when the normalization methods were applied. In the subanalysis for the MSA-p patients with a longer disease duration, the performance of the algorithm improved.

CONCLUSION

This algorithm detected the putaminal lateral margin well, provided insight into the iron distribution of the putaminal rim of MSA-p, and demonstrated good performance in differentiating MSA-p from PD.

摘要

目的

壳核铁沉积是有助于将帕金森叠加型多系统萎缩(MSA-p)与帕金森病(PD)相鉴别的重要特征。以往大多数研究采用视觉检查或手动操作的定量方法进行这种鉴别。我们研究了一种使用3T磁共振 susceptibility加权成像的新型半自动诊断算法对MSA-p的价值。

方法

本研究纳入了26例MSA-p患者、68例PD患者和41例正常对照(NC)受试者。该算法分两步开发:1)确定包含明显壳核边缘的图像;2)计算反映铁浓度的相移值。下一步是在几种组合中确定最佳鉴别条件。使用每个受试者的最高相移值来评估最有效的诊断集。

结果

每组中原始相移值沿壳核外侧边缘呈现。它显示出从前到后的梯度,在MSA-p中最常被识别。使用前5个相移值的平均值进行归一化。当应用归一化方法时,在3或4个垂直像素和一侧优势的组合下,MSA-p与PD的受试者操作特征曲线下面积最高(0.874,灵敏度80.8%,特异性86.7%)。在对病程较长的MSA-p患者的亚分析中,该算法的性能有所提高。

结论

该算法能很好地检测壳核外侧边缘,深入了解MSA-p壳核边缘的铁分布,并在区分MSA-p与PD方面表现良好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed56/9536910/145d4cce4432/jmd-21178f1.jpg

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