期刊
NEUROIMAGE-CLINICAL
卷 18, 期 -, 页码 167-177出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2018.01.019
关键词
Alzheimer's disease dementia; Clinical setting; Erlangen Score; Frontotemporal dementia; Prognosis
类别
资金
- Italian Ministry of Health (Ricerca Finalizzata Progetto Reti Nazionale) [AD NET-2011-02346784]
- IVASCOMAR project Identificazione, validazione e sviluppo commerciale di nuovi biomarcatori diagnostici prognostici per malattie complesse [CTN01_00177_165430]
- Fondazione Eli-Lilly
- Italian Ministry of Health
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. Methods: 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). A beta 42, 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. Results: The multivariate logistic model identified FDG-PET AD SPM classification (Exp beta = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF A beta 42 (Exp beta = 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 (Exp beta = 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 (Exp beta = 17.9, 95% C.I. 4.55-70.46, p < 0.001). Conclusions: 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据