Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Neuroimage Clin. 2018 Jan 28;18:167-177. doi: 10.1016/j.nicl.2018.01.019. eCollection 2018.
BACKGROUND/AIMS: In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias.
We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated.
The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The "FTD" SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1-63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55-70.46, p < 0.001).
Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.
背景/目的：在这项多中心临床研究中，我们评估了优化的 FDG-PET 脑代谢和 CSF 分类程序在预测或排除向阿尔茨海默病（AD）痴呆和非 AD 痴呆转化方面的准确性。
我们纳入了 80 名 MCI 患者，他们在入组时进行了神经学和神经心理学评估、FDG-PET 扫描和 CSF 测量，并进行了临床随访。FDG-PET 数据采用经过验证的基于体素的 SPM 方法进行分析。根据疾病特异性模式，由五名影像学专家对生成的单个受试者 SPM 图谱进行分类，分为“典型 AD”、“非典型 AD”（即后部皮质萎缩、非对称 logopenic AD 变体、额叶 AD 变体）、“非 AD”（即行为变异型额颞叶痴呆、皮质基底节变性、语义变异型额颞叶痴呆；路易体痴呆）或“阴性”模式。为了进行统计分析，将个体模式分为“AD 痴呆与非 AD 痴呆（所有疾病）”或“FTD 与非 FTD（所有疾病）”。Aβ42、总 Tau 和磷酸化 Tau CSF 水平分为二分类，并使用 Erlangen 评分算法。多元逻辑模型测试了 FDG-PET-SPM 和 CSF 二分类的预后准确性。评估了 Erlangen 评分和 FDG-PET SPM 分类辅助的 Erlangen 评分的准确性。
多元逻辑模型确定了 FDG-PET“AD”SPM 分类（Expβ=19.35，95%置信区间 4.8-77.8，p<0.001）和 CSF Aβ42（Expβ=6.5，95%置信区间 1.64-25.43，p<0.05）是从 MCI 向 AD 痴呆转化的最佳预测指标。“FTD”SPM 模式显著预测了随访时向 FTD 痴呆的转化（Expβ=14，95%置信区间 3.1-63，p<0.001）。总体而言，FDG-PET-SPM 分类是最准确的生物标志物，能够正确区分从 MCI 转化为 AD 或 FTD 痴呆的患者，以及保持稳定或恢复正常认知的患者（Expβ=17.9，95%置信区间 4.55-70.46，p<0.001）。
我们的研究结果支持 FDG-PET-SPM 分类在预测前驱期 MCI 阶段向不同痴呆状态进展以及排除进展方面的重要作用，其表现优于 CSF 生物标志物。
