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

SP Caminiti, T Ballarini, A Sala, C Cerami, L Presotto, R Santangelo, F Fallanca, EG Vanoli, L Gianolli, S Iannaccone, G Magnani, D Perani, L Parnetti, P Eusebi, A Picco, BIOMARKAPD Project

Research output: Contribution to journalArticle

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. © 2018
Original languageEnglish
Pages (from-to)167-177
Number of pages11
JournalNeuroImage: Clinical
Volume18
Issue number1
DOIs
Publication statusPublished - 2018

Fingerprint

Dementia
Alzheimer Disease
Biomarkers
Logistic Models
Lewy Body Disease
Semantics
Positron-Emission Tomography
Cognition
Multicenter Studies
Atrophy
Brain

Cite this

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. / Caminiti, SP; Ballarini, T; Sala, A; Cerami, C; Presotto, L; Santangelo, R; Fallanca, F; Vanoli, EG; Gianolli, L; Iannaccone, S; Magnani, G; Perani, D; Parnetti, L; Eusebi, P; Picco, A; Project, BIOMARKAPD.

In: NeuroImage: Clinical, Vol. 18, No. 1, 2018, p. 167-177.

Research output: Contribution to journalArticle

Caminiti, SP, Ballarini, T, Sala, A, Cerami, C, Presotto, L, Santangelo, R, Fallanca, F, Vanoli, EG, Gianolli, L, Iannaccone, S, Magnani, G, Perani, D, Parnetti, L, Eusebi, P, Picco, A & Project, BIOMARKAPD 2018, 'FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort', NeuroImage: Clinical, vol. 18, no. 1, pp. 167-177. https://doi.org/10.1016/j.nicl.2018.01.019
Caminiti, SP ; Ballarini, T ; Sala, A ; Cerami, C ; Presotto, L ; Santangelo, R ; Fallanca, F ; Vanoli, EG ; Gianolli, L ; Iannaccone, S ; Magnani, G ; Perani, D ; Parnetti, L ; Eusebi, P ; Picco, A ; Project, BIOMARKAPD. / FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. In: NeuroImage: Clinical. 2018 ; Vol. 18, No. 1. pp. 167-177.
@article{87a0f8433b6d424e8bca6776f24f2f81,
title = "FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort",
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. {\circledC} 2018",
author = "SP Caminiti and T Ballarini and A Sala and C Cerami and L Presotto and R Santangelo and F Fallanca and EG Vanoli and L Gianolli and S Iannaccone and G Magnani and D Perani and L Parnetti and P Eusebi and A Picco and BIOMARKAPD Project",
year = "2018",
doi = "10.1016/j.nicl.2018.01.019",
language = "English",
volume = "18",
pages = "167--177",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
publisher = "ELSEVIER SCI LTD",
number = "1",

}

TY - JOUR

T1 - FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

AU - Caminiti, SP

AU - Ballarini, T

AU - Sala, A

AU - Cerami, C

AU - Presotto, L

AU - Santangelo, R

AU - Fallanca, F

AU - Vanoli, EG

AU - Gianolli, L

AU - Iannaccone, S

AU - Magnani, G

AU - Perani, D

AU - Parnetti, L

AU - Eusebi, P

AU - Picco, A

AU - Project, BIOMARKAPD

PY - 2018

Y1 - 2018

N2 - 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. © 2018

AB - 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. © 2018

U2 - 10.1016/j.nicl.2018.01.019

DO - 10.1016/j.nicl.2018.01.019

M3 - Article

VL - 18

SP - 167

EP - 177

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

IS - 1

ER -