Max Planck Institute for Neurological Research, Cologne, Germany.
Dement Geriatr Cogn Disord. 2009;28(3):259-66. doi: 10.1159/000241879. Epub 2009 Sep 25.
We investigated the performance of FDG PET using an automated procedure for discrimination between Alzheimer's disease (AD) and controls, and studied the influence of demographic and technical factors.
FDG PET data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) [102 controls (76.0 +/- 4.9 years) and 89 AD patients (75.7 +/- 7.6 years, MMSE 23.5 +/- 2.1) and the Network for Standardisation of Dementia Diagnosis (NEST-DD) [36 controls (62.2 +/- 5.0 years) and 237 AD patients (70.8 +/- 8.3 years, MMSE 20.9 +/- 4.4). The procedure created t-maps of abnormal voxels. The sum of t-values in predefined areas that are typically affected by AD (AD t-sum) provided a measure of scan abnormality associated with a preset threshold for discrimination between patients and controls.
AD patients had much higher AD t-sum scores compared to controls (p < 0.01), which were significantly related to dementia severity (ADNI: r = -0.62, p < 0.01; NEST-DD: r = -0.59, p < 0.01). Early-onset AD patients had significantly higher AD t-sum scores than late-onset AD patients (p < 0.01). Differences between databases were mainly due to different age distributions. The predefined AD t-sum threshold yielded a sensitivity and specificity of 83 and 78% in ADNI and 78 and 94% in NEST-DD, respectively.
The automated FDG PET analysis procedure provided good discrimination power, and was most accurate for early-onset AD.
我们研究了使用自动程序区分阿尔茨海默病（AD）和对照组的 FDG PET 的性能，并研究了人口统计学和技术因素的影响。
从阿尔茨海默病神经影像学倡议（ADNI）[102 名对照组（76.0 ± 4.9 岁）和 89 名 AD 患者（75.7 ± 7.6 岁，MMSE 23.5 ± 2.1）和标准化痴呆诊断网络（NEST-DD）[36 名对照组（62.2 ± 5.0 岁）和 237 名 AD 患者（70.8 ± 8.3 岁，MMSE 20.9 ± 4.4）中获得 FDG PET 数据。该程序创建了异常体素的 t 映射。在通常受 AD 影响的预定义区域中 t 值的总和（AD t 总和）提供了与预设阈值相关的扫描异常的度量，用于区分患者和对照组。
AD 患者的 AD t 总和评分明显高于对照组（p < 0.01），与痴呆严重程度显著相关（ADNI：r = -0.62，p < 0.01；NEST-DD：r = -0.59，p < 0.01）。早发性 AD 患者的 AD t 总和评分明显高于晚发性 AD 患者（p < 0.01）。数据库之间的差异主要是由于年龄分布不同。预定义的 AD t 总和阈值在 ADNI 中分别产生了 83%和 78%的敏感性和特异性，在 NEST-DD 中分别产生了 78%和 94%的敏感性和特异性。
自动 FDG PET 分析程序提供了良好的区分能力，并且对早发性 AD 最准确。
