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Detecting frontotemporal dementia syndromes using MRI biomarkers

BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal de...

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Autores principales: Bruun, Marie, Koikkalainen, Juha, Rhodius-Meester, Hanneke F.M., Baroni, Marta, Gjerum, Le, van Gils, Mark, Soininen, Hilkka, Remes, Anne M., Hartikainen, Päivi, Waldemar, Gunhild, Mecocci, Patrizia, Barkhof, Frederik, Pijnenburg, Yolande, van der Flier, Wiesje M., Hasselbalch, Steen G., Lötjönen, Jyrki, Frederiksen, Kristian S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369219/
https://www.ncbi.nlm.nih.gov/pubmed/30743135
http://dx.doi.org/10.1016/j.nicl.2019.101711
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author Bruun, Marie
Koikkalainen, Juha
Rhodius-Meester, Hanneke F.M.
Baroni, Marta
Gjerum, Le
van Gils, Mark
Soininen, Hilkka
Remes, Anne M.
Hartikainen, Päivi
Waldemar, Gunhild
Mecocci, Patrizia
Barkhof, Frederik
Pijnenburg, Yolande
van der Flier, Wiesje M.
Hasselbalch, Steen G.
Lötjönen, Jyrki
Frederiksen, Kristian S.
author_facet Bruun, Marie
Koikkalainen, Juha
Rhodius-Meester, Hanneke F.M.
Baroni, Marta
Gjerum, Le
van Gils, Mark
Soininen, Hilkka
Remes, Anne M.
Hartikainen, Päivi
Waldemar, Gunhild
Mecocci, Patrizia
Barkhof, Frederik
Pijnenburg, Yolande
van der Flier, Wiesje M.
Hasselbalch, Steen G.
Lötjönen, Jyrki
Frederiksen, Kristian S.
author_sort Bruun, Marie
collection PubMed
description BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.
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spelling pubmed-63692192019-02-20 Detecting frontotemporal dementia syndromes using MRI biomarkers Bruun, Marie Koikkalainen, Juha Rhodius-Meester, Hanneke F.M. Baroni, Marta Gjerum, Le van Gils, Mark Soininen, Hilkka Remes, Anne M. Hartikainen, Päivi Waldemar, Gunhild Mecocci, Patrizia Barkhof, Frederik Pijnenburg, Yolande van der Flier, Wiesje M. Hasselbalch, Steen G. Lötjönen, Jyrki Frederiksen, Kristian S. Neuroimage Clin Regular Article BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Elsevier 2019-02-04 /pmc/articles/PMC6369219/ /pubmed/30743135 http://dx.doi.org/10.1016/j.nicl.2019.101711 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Regular Article
Bruun, Marie
Koikkalainen, Juha
Rhodius-Meester, Hanneke F.M.
Baroni, Marta
Gjerum, Le
van Gils, Mark
Soininen, Hilkka
Remes, Anne M.
Hartikainen, Päivi
Waldemar, Gunhild
Mecocci, Patrizia
Barkhof, Frederik
Pijnenburg, Yolande
van der Flier, Wiesje M.
Hasselbalch, Steen G.
Lötjönen, Jyrki
Frederiksen, Kristian S.
Detecting frontotemporal dementia syndromes using MRI biomarkers
title Detecting frontotemporal dementia syndromes using MRI biomarkers
title_full Detecting frontotemporal dementia syndromes using MRI biomarkers
title_fullStr Detecting frontotemporal dementia syndromes using MRI biomarkers
title_full_unstemmed Detecting frontotemporal dementia syndromes using MRI biomarkers
title_short Detecting frontotemporal dementia syndromes using MRI biomarkers
title_sort detecting frontotemporal dementia syndromes using mri biomarkers
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369219/
https://www.ncbi.nlm.nih.gov/pubmed/30743135
http://dx.doi.org/10.1016/j.nicl.2019.101711
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