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FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.
Neuroimage Clin 2018; 18:167-177NC

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.

Authors+Show Affiliations

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.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. Clinical Neuroscience Department, San Raffaele Turro Hospital, Milan, Italy.Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.Department of Neurology and INSPE, San Raffaele Scientific Institute, Milan, Italy.Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy.Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy.Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy.Clinical Neuroscience Department, San Raffaele Turro Hospital, Milan, Italy.Department of Neurology and INSPE, San Raffaele Scientific Institute, Milan, Italy.Vita-Salute San Raffaele University, Milan, Italy. Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy.No affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29387532

Citation

Caminiti, Silvia Paola, et al. "FDG-PET and CSF Biomarker Accuracy in Prediction of Conversion to Different Dementias in a Large Multicentre MCI Cohort." NeuroImage. Clinical, vol. 18, 2018, pp. 167-177.
Caminiti SP, Ballarini T, Sala A, et al. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. Neuroimage Clin. 2018;18:167-177.
Caminiti, S. P., Ballarini, T., Sala, A., Cerami, C., Presotto, L., Santangelo, R., ... Perani, D. (2018). FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. NeuroImage. Clinical, 18, pp. 167-177. doi:10.1016/j.nicl.2018.01.019.
Caminiti SP, et al. FDG-PET and CSF Biomarker Accuracy in Prediction of Conversion to Different Dementias in a Large Multicentre MCI Cohort. Neuroimage Clin. 2018;18:167-177. PubMed PMID: 29387532.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. AU - Caminiti,Silvia Paola, AU - Ballarini,Tommaso, AU - Sala,Arianna, AU - Cerami,Chiara, AU - Presotto,Luca, AU - Santangelo,Roberto, AU - Fallanca,Federico, AU - Vanoli,Emilia Giovanna, AU - Gianolli,Luigi, AU - Iannaccone,Sandro, AU - Magnani,Giuseppe, AU - Perani,Daniela, AU - ,, Y1 - 2018/01/28/ PY - 2017/07/31/received PY - 2017/11/15/revised PY - 2018/01/18/accepted PY - 2018/2/2/entrez PY - 2018/2/2/pubmed PY - 2019/1/11/medline KW - AD, Alzheimer's disease KW - AUC, area under curve KW - Alzheimer's disease dementia KW - CBD, corticobasal degeneration KW - CDR, Clinical Dementia Rating KW - CSF, cerebrospinal fluid KW - Clinical setting KW - DLB, dementia with Lewy bodies KW - EANM, European Association of Nuclear Medicine KW - Erlangen Score KW - FDG, fluorodeoxyglucose KW - FTD, frontotemporal dementia KW - Frontotemporal dementia KW - LR+, positive likelihood ratio KW - LR-, negative likelihood ratio KW - MCI, mild cognitive impairment KW - PET, positron emission tomography KW - PSP, progressive supranuclear palsy KW - Prognosis KW - aMCI, single-domain amnestic mild cognitive impairment KW - bvFTD, behavioral variant of frontotemporal dementia KW - md aMCI, multi-domain amnestic mild cognitive impairment KW - md naMCI, multi-domain non-amnestic mild cognitive impairment KW - naMCI, single-domain non-amnestic mild cognitive impairment KW - p-tau, phosphorylated tau KW - t-tau, total tau SP - 167 EP - 177 JF - NeuroImage. Clinical JO - Neuroimage Clin VL - 18 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. SN - 2213-1582 UR - https://www.unboundmedicine.com/medline/citation/29387532/FDG_PET_and_CSF_biomarker_accuracy_in_prediction_of_conversion_to_different_dementias_in_a_large_multicentre_MCI_cohort_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S2213-1582(18)30019-6 DB - PRIME DP - Unbound Medicine ER -