Predicting outcome and recovery after stroke with lesions extracted from MRI images☆

Here, we present and validate a method that lets us predict the severity of cognitive impairments after stroke, and the likely course of recovery over time. Our approach employs (a) a database that records the behavioural scores from a large population of patients who have, collectively, incurred a...

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Main Authors:	Hope, Thomas M.H., Seghier, Mohamed L., Leff, Alex P., Price, Cathy J.
Format:	Online
Language:	English
Published:	Elsevier 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778268/

id	pubmed-3778268
recordtype	oai_dc
spelling	pubmed-37782682013-10-31 Predicting outcome and recovery after stroke with lesions extracted from MRI images☆ Hope, Thomas M.H. Seghier, Mohamed L. Leff, Alex P. Price, Cathy J. Article Here, we present and validate a method that lets us predict the severity of cognitive impairments after stroke, and the likely course of recovery over time. Our approach employs (a) a database that records the behavioural scores from a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions, (b) an automated procedure to convert structural brain scans from those patients into three-dimensional images of their lesions, and (c) a system to learn the relationship between patients' lesions, demographics and behavioural capacities at different times post-stroke. Validation against data collected from 270 stroke patients suggests that our first set of variables yielded predictions that match or exceed the predictive power reported in any comparable work in the available literature. Predictions are likely to improve when other determinants of recovery are included in the system. Many behavioural outcomes after stroke could be predicted using the proposed approach. Elsevier 2013-03-22 /pmc/articles/PMC3778268/ /pubmed/24179796 http://dx.doi.org/10.1016/j.nicl.2013.03.005 Text en © 2013 The Authors http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Hope, Thomas M.H. Seghier, Mohamed L. Leff, Alex P. Price, Cathy J.
spellingShingle	Hope, Thomas M.H. Seghier, Mohamed L. Leff, Alex P. Price, Cathy J. Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
author_facet	Hope, Thomas M.H. Seghier, Mohamed L. Leff, Alex P. Price, Cathy J.
author_sort	Hope, Thomas M.H.
title	Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
title_short	Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
title_full	Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
title_fullStr	Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
title_full_unstemmed	Predicting outcome and recovery after stroke with lesions extracted from MRI images☆
title_sort	predicting outcome and recovery after stroke with lesions extracted from mri images☆
description	Here, we present and validate a method that lets us predict the severity of cognitive impairments after stroke, and the likely course of recovery over time. Our approach employs (a) a database that records the behavioural scores from a large population of patients who have, collectively, incurred a comprehensive range of focal brain lesions, (b) an automated procedure to convert structural brain scans from those patients into three-dimensional images of their lesions, and (c) a system to learn the relationship between patients' lesions, demographics and behavioural capacities at different times post-stroke. Validation against data collected from 270 stroke patients suggests that our first set of variables yielded predictions that match or exceed the predictive power reported in any comparable work in the available literature. Predictions are likely to improve when other determinants of recovery are included in the system. Many behavioural outcomes after stroke could be predicted using the proposed approach.
publisher	Elsevier
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778268/
_version_	1612013002646618112

Predicting outcome and recovery after stroke with lesions extracted from MRI images☆

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