[논문]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, Pä; ivi; Waldemar, Gunhild; Mecocci, Patrizia; Barkhof, Frederik; Pijnenburg, Yolande; van der Flier, Wiesje M.; Hasselbalch, Steen G.; Lö; tjö; nen, Jyrki; Frederiksen, Kristian S.

doi:10.1016/j.nicl.2019.101711

Detecting frontotemporal dementia syndromes using MRI biomarkers 원문보기

NeuroImage : Clinical, v.22, 2019년, pp.101711 -

Bruun, Marie (Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen) , Koikkalainen, Juha (Combinostics Ltd.) , Rhodius-Meester, Hanneke F.M. (Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam) , Baroni, Marta (Institute of Gerontology and Geriatrics, University of Perugia) , Gjerum, Le (Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen) , van Gils, Mark (VTT Technical Research Center of Finland Ltd) , Soininen, Hilkka (Institute of Clinical Medicine, Neurology, University of Eastern Finland) , Remes, Anne M. (Unit of Clinical Neuroscience, Neurology, University of Oulu) , Hartikainen, Päivi (Neurocenter, neurology, Kuopio University Hospital) , Waldemar, Gunhild (Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen Denmark<) , Mecocci, Patrizia , Barkhof, Frederik , Pijnenburg, Yolande , van der Flier, Wiesje M. , Hasselbalch, Steen G. , Lötjönen, Jyrki , Frederiksen, Kristian S.

Abstract ▼ AI-Helper

Abstract 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. Highlights Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes. API differentiated FTD from other diagnostic groups with AUC of 83%. ASI and TPL showed highest performance for PPA subtypes. A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.

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Detecting frontotemporal dementia syndromes using MRI biomarkers 원문보기

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