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. 2018 Jan 28:18:167-177.
doi: 10.1016/j.nicl.2018.01.019. eCollection 2018.

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

Silvia Paola Caminiti  1 , Tommaso Ballarini  2 , Arianna Sala  1 , Chiara Cerami  3 , Luca Presotto  2 , Roberto Santangelo  4 , Federico Fallanca  5 , Emilia Giovanna Vanoli  5 , Luigi Gianolli  5 , Sandro Iannaccone  6 , Giuseppe Magnani  4 , Daniela Perani  7 ; BIOMARKAPD Project
Collaborators, Affiliations

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

Silvia Paola Caminiti et al. Neuroimage Clin. .

Abstract

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)". 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.

Results: 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).

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.

Keywords: AD, Alzheimer's disease; AUC, area under curve; Alzheimer's disease dementia; CBD, corticobasal degeneration; CDR, Clinical Dementia Rating; CSF, cerebrospinal fluid; Clinical setting; DLB, dementia with Lewy bodies; EANM, European Association of Nuclear Medicine; Erlangen Score; FDG, fluorodeoxyglucose; FTD, frontotemporal dementia; Frontotemporal dementia; LR+, positive likelihood ratio; LR-, negative likelihood ratio; MCI, mild cognitive impairment; PET, positron emission tomography; PSP, progressive supranuclear palsy; Prognosis; aMCI, single-domain amnestic mild cognitive impairment; bvFTD, behavioral variant of frontotemporal dementia; md aMCI, multi-domain amnestic mild cognitive impairment; md naMCI, multi-domain non-amnestic mild cognitive impairment; naMCI, single-domain non-amnestic mild cognitive impairment; p-tau, phosphorylated tau; t-tau, total tau.

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Figures

Fig. 1
Fig. 1
Concordance between baseline CSF dichotomous values and FDG-PET SPM classification. For each of the three FDG-PET based categories (AD, FTD and negative patterns), the percentage of subjects with positive CSF assessment is reported. CSF positivity for Aβ42, t-Tau and p-Tau is highly prevalent among MCI with an AD FDG-PET pattern at baseline. A minor portion of FTD patients show increased t-Tau and p-Tau levels. Aβ42 positivity characterize also ca. half of the MCI with a negative FDG-PET pattern.
Fig. 2
Fig. 2
Positive and negative likelihood ratio (LR+ and LR−) for correct classification of MCI subjects converting to AD dementia. LR+ >5 indicates that the biomarker positive classification is associated with the disease occurrence. LR− <0.2 indicates a relevant association between the negative biomarker classification and the absence of the dementia condition at follow up. LR values are represented on a logarithmic scale.
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