Texture features of magnetic resonance ...
Type de document :
Article dans une revue scientifique: Article original
PMID :
URL permanente :
Titre :
Texture features of magnetic resonance images: an early marker of post-stroke cognitive impairment
Auteur(s) :
Betrouni, Nacim [Auteur]
Yasmina, Moussaoui [Auteur]
Bombois, Stephanie [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Petrault, Maud [Auteur]
Dondaine, Thibaut [Auteur]
LACHAUD, cedrick [Auteur]
Laloux, Charlotte [Auteur]
Mendyk, Anne-Marie [Auteur]
Henon, Hilde [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Bordet, Regis [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Yasmina, Moussaoui [Auteur]
Bombois, Stephanie [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Petrault, Maud [Auteur]
Dondaine, Thibaut [Auteur]
LACHAUD, cedrick [Auteur]
Laloux, Charlotte [Auteur]
Mendyk, Anne-Marie [Auteur]
Henon, Hilde [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Bordet, Regis [Auteur]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U 1171 - EA 1046 [TCDV]
Troubles cognitifs dégénératifs et vasculaires - U1171
Titre de la revue :
Translational stroke research
Nom court de la revue :
Transl Stroke Res
Date de publication :
2019-11-01
ISSN :
1868-601X
Mot(s)-clé(s) en anglais :
Neuron loss
Stroke
Predictive features
Radiomics
Texture analysis
Cognitive impairment
Stroke
Predictive features
Radiomics
Texture analysis
Cognitive impairment
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of ...
Lire la suite >Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.Lire moins >
Lire la suite >Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
CNRS
Inserm
Université de Lille
CNRS
Inserm
Université de Lille
Collections :
Équipe(s) de recherche :
Troubles cognitifs dégénératifs et vasculaires
Date de dépôt :
2019-11-27T13:36:14Z