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Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model

2-[(18)F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[(18)F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studi...

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Autores principales: Gjerum, Le, Frederiksen, Kristian Steen, Henriksen, Otto Mølby, Law, Ian, Bruun, Marie, Simonsen, Anja Hviid, Mecocci, Patrizia, Baroni, Marta, Dottorini, Massimo Eugenio, Koikkalainen, Juha, Lötjönen, Jyrki, Hasselbalch, Steen Gregers
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229490/
https://www.ncbi.nlm.nih.gov/pubmed/32417727
http://dx.doi.org/10.1016/j.nicl.2020.102267
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author Gjerum, Le
Frederiksen, Kristian Steen
Henriksen, Otto Mølby
Law, Ian
Bruun, Marie
Simonsen, Anja Hviid
Mecocci, Patrizia
Baroni, Marta
Dottorini, Massimo Eugenio
Koikkalainen, Juha
Lötjönen, Jyrki
Hasselbalch, Steen Gregers
author_facet Gjerum, Le
Frederiksen, Kristian Steen
Henriksen, Otto Mølby
Law, Ian
Bruun, Marie
Simonsen, Anja Hviid
Mecocci, Patrizia
Baroni, Marta
Dottorini, Massimo Eugenio
Koikkalainen, Juha
Lötjönen, Jyrki
Hasselbalch, Steen Gregers
author_sort Gjerum, Le
collection PubMed
description 2-[(18)F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[(18)F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[(18)F]FDG-PET data. The objective of the study was to evaluate 2-[(18)F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[(18)F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[(18)F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[(18)F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[(18)F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.
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spelling pubmed-72294902020-05-20 Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model Gjerum, Le Frederiksen, Kristian Steen Henriksen, Otto Mølby Law, Ian Bruun, Marie Simonsen, Anja Hviid Mecocci, Patrizia Baroni, Marta Dottorini, Massimo Eugenio Koikkalainen, Juha Lötjönen, Jyrki Hasselbalch, Steen Gregers Neuroimage Clin Regular Article 2-[(18)F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[(18)F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[(18)F]FDG-PET data. The objective of the study was to evaluate 2-[(18)F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[(18)F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[(18)F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[(18)F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[(18)F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD. Elsevier 2020-04-24 /pmc/articles/PMC7229490/ /pubmed/32417727 http://dx.doi.org/10.1016/j.nicl.2020.102267 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Gjerum, Le
Frederiksen, Kristian Steen
Henriksen, Otto Mølby
Law, Ian
Bruun, Marie
Simonsen, Anja Hviid
Mecocci, Patrizia
Baroni, Marta
Dottorini, Massimo Eugenio
Koikkalainen, Juha
Lötjönen, Jyrki
Hasselbalch, Steen Gregers
Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title_full Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title_fullStr Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title_full_unstemmed Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title_short Evaluating 2-[(18)F]FDG-PET in differential diagnosis of dementia using a data-driven decision model
title_sort evaluating 2-[(18)f]fdg-pet in differential diagnosis of dementia using a data-driven decision model
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229490/
https://www.ncbi.nlm.nih.gov/pubmed/32417727
http://dx.doi.org/10.1016/j.nicl.2020.102267
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