Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/103347
DC Field | Value | Language |
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dc.contributor.author | Lavrador, Rui Filipe David | - |
dc.contributor.author | Júlio, Filipa | - |
dc.contributor.author | Januário, Cristina | - |
dc.contributor.author | Castelo Branco, Miguel | - |
dc.contributor.author | Caetano, Gina | - |
dc.date.accessioned | 2022-11-08T09:47:21Z | - |
dc.date.available | 2022-11-08T09:47:21Z | - |
dc.date.issued | 2022-04-28 | - |
dc.identifier.issn | 2075-4426 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/103347 | - |
dc.description.abstract | The purpose of this study was to classify Huntington's disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average > 20 years from estimated disease onset), eleven early-manifest HD (Early-HD) patients, and eighteen healthy controls (HC) participated in the study. We compared three feature selection approaches: (i) whole-brain segmented grey matter (GM; voxel-based measure) or fractional anisotropy (FA) values; (ii) GM or FA values from subcortical regions-of-interest (caudate, putamen, pallidum); and (iii) automated selection of GM or FA values with the algorithm Relief-F. We assessed single- and multi-kernel approaches to classify combined GM and FA measures. Significant classifications were achieved between Early-HD and Pre-HD or HC individuals (accuracy: generally, 85% to 95%), and between Pre-HD and controls for the feature FA of the caudate ROI (74% accuracy). The combination of GM and FA measures did not result in higher performances. We demonstrate evidence on the high sensitivity of FA for the classification of the earliest Pre-HD stages, and successful distinction between HD stages. | pt |
dc.language.iso | eng | pt |
dc.relation | PTDC/SAU-ENB/112306/2009 | pt |
dc.relation | POCI-01-0145-FEDER-007440 | pt |
dc.relation | UIDP/50009/2020 | pt |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/04950/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Huntington’s disease | pt |
dc.subject | grey matter density | pt |
dc.subject | fractional anisotropy | pt |
dc.subject | classification | pt |
dc.subject | support vector machine | pt |
dc.subject | basal ganglia | pt |
dc.title | Classification of Huntington's Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging | pt |
dc.type | article | - |
degois.publication.firstPage | 704 | pt |
degois.publication.issue | 5 | pt |
degois.publication.title | Journal of Personalized Medicine | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/jpm12050704 | pt |
degois.publication.volume | 12 | pt |
dc.date.embargo | 2022-04-28 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Laboratory of Robotics and Engineering Systems | - |
crisitem.project.grantno | Coimbra Institute for Biomedical Imaging and Translational Research | - |
crisitem.author.researchunit | CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research | - |
crisitem.author.researchunit | CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research | - |
crisitem.author.orcid | 0000-0001-6075-6887 | - |
crisitem.author.orcid | 0000-0001-5402-3978 | - |
crisitem.author.orcid | 0000-0003-4364-6373 | - |
Appears in Collections: | I&D CIBIT - Artigos em Revistas Internacionais FPCEUC - Artigos em Revistas Internacionais I&D ICNAS - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Classification-of-Huntingtons-Disease-Stage-with-Features-Derived-from-Structural-and-DiffusionWeighted-ImagingJournal-of-Personalized-Medicine.pdf | 2.81 MB | Adobe PDF | View/Open |
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