Manera, Ana L; Dadar, Mahsa; Van Swieten, John Cornelis; Borroni, Barbara; Sanchez-Valle, Raquel; Moreno, Fermin; Laforce, Robert; Graff, Caroline; Synofzik, Matthis; Galimberti, Daniela; +21 more... Rowe, James Benedict; Masellis, Mario; Tartaglia, Maria Carmela; Finger, Elizabeth; Vandenberghe, Rik; de Mendonca, Alexandre; Tagliavini, Fabrizio; Santana, Isabel; Butler, Christopher R; Gerhard, Alex; Danek, Adrian; Levin, Johannes; Otto, Markus; Frisoni, Giovanni; Ghidoni, Roberta; Sorbi, Sandro; Rohrer, Jonathan Daniel; Ducharme, Simon; Collins, D Louis; FTLDNI investigators; GENFI Consortium; (2021) MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia. Journal of neurology, neurosurgery, and psychiatry, 92 (6). pp. 608-616. ISSN 0022-3050 DOI: https://doi.org/10.1136/jnnp-2020-324106
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Abstract
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.
Item Type | Article |
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PubMed ID | 33722819 |
Elements ID | 165681 |
Official URL | http://dx.doi.org/10.1136/jnnp-2020-324106 |
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Filename: Rohrer_MRI-data-driven-algorithm-for-the-diagnosis-of-behavioural-variant-frontotemporal-dementia_AAM.pdf
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Licence: Creative Commons: Attribution-Noncommercial 4.0
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