Marie Bruun, Juha Koikkalainen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Mark van Gils, Hilkka Soininen, Anne M. Remes, Päivi Hartikainen, Gunhild Waldemar, Patrizia Mecocci, Frederik Barkhof, Yolande Pijnenburg, Wiesje M. van der Flier, Steen G. Hasselbalch, Jyrki Lötjönen, Kristian S. Frederiksen, Detecting frontotemporal dementia syndromes using MRI biomarkers, NeuroImage: Clinical, Volume 22, 2019, 101711, ISSN 2213-1582, https://doi.org/10.1016/j.nicl.2019.101711
Detecting frontotemporal dementia syndromes using MRI biomarkers
|Author:||Bruun, Marie1; Koikkalainen, Juha2; Rhodius-Meester, Hanneke F. M.3;|
1Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
2Combinostics Ltd., Tampere, Finland
3Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
4Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
5VTT Technical Research Center of Finland Ltd, Tampere, Finland
6Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
7Neurocenter, neurology, Kuopio University Hospital, Kuopio, Finland
8Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
9Medical Research Center, Oulu University Hospital, Oulu, Finland
10Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
11UCL institutes of Neurology and Healthcare Engineering, London, UK
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019092629915
|Publish Date:|| 2019-09-26
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3124 Neurology and psychiatry
3126 Surgery, anesthesiology, intensive care, radiology
This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd, GE Healthcare Ltd, Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre.
© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).