在自动脑缩估计中的正常队列:包括多少健康受试者?
Christian Rubbert1, Luisa Wolf2, Marius Vach2
1Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany. christian.rubbert@med.uni-duesseldorf.de.
European radiology
|January 8, 2024
概括
正常的队列大小影响自动脑缩估计,需要至少15名受试者以保持一致性. 不同的正常队列不会影响阿尔茨海默病诊断中的区域性缩估计准确性.
科学领域:
- 神经成像是一种神经成像.
- 放射学 放射学是一门学科.
- 生物统计学 生物统计学
背景情况:
- 使用MRI的自动脑缩估计依赖于与正常队列 (NCs) 的比较.
- 对于可靠的缩估计,NCs的最佳大小和可变性尚未得到很好的定义.
- 了解这些因素对于准确的阿尔茨海默病 (AD) 诊断至关重要.
研究的目的:
- 调查正常队列 (NC) 大小对基于MRI的自动脑缩估计的影响.
- 评估使用不同的NC对缩量定量的准确性的影响.
- 确定NC所需的最低受试者数量,以一致估计大脑缩.
主要方法:
- 从公共数据集中创建了一个大型的聚合NC (3945个受试者).
- 对于阿尔茨海默病患者,使用模板与增量大小的NCs (3-100名受试者) 生成了Voxel-wise灰色物质缩图.
- 确定了用于一致估计的最小NC大小,并将AD/HC歧视的不同NC之间比较了缩图.
主要成果:
- 在15名受试者身上发现了最大膝盖点,表明一致估计所需的最低受试者数量.
- 通过21个AD/21 HC的验证,在每个NC中显示了足够的受试者.
- 观察到高的读者间一致性 (Kappa=0.98) 和NC之间没有显著的诊断差异 (ICC=0.91).
结论:
- 特定于年龄和性别的正常模板应包括至少15名受试者,以可靠地估计大脑缩.
- 区域性缩的定性解释使得准确的AD诊断具有高的读者间一致性,无论NC的组成如何.
- 这项研究为优化NC用于自动化神经成像分析提供了关键的见解.
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