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Title
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MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
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Author
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Institution/Organisation
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FTLDNI Investigators GENFI Consortium
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Abstract
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| Introduction Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. Methods A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. Results Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. Conclusion Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database. |
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Language
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English
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Source (journal)
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Journal of neurology, neurosurgery and psychiatry. - London
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Publication
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London
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2021
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ISSN
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0022-3050
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DOI
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10.1136/JNNP-2020-324106
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Volume/pages
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92
:6
(2021)
, p. 608-616
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ISI
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000654242100009
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Pubmed ID
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33722819
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Full text (Publisher's DOI)
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Full text (open access)
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