Automated brain MRI metrics in the EPIRMEX cohort of preterm newborns: Correlation with the neurodevelopmental outcome at 2 years.

Benchmarking Brain / diagnostic imaging Humans Infant Infant, Newborn Infant, Premature Infant, Premature, Diseases Magnetic Resonance Imaging
Brain Cohort studies Magnetic resonance imaging Premature White matter

Journal

Diagnostic and interventional imaging
ISSN: 2211-5684
Titre abrégé: Diagn Interv Imaging
Pays: France
ID NLM: 101568499

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 02 07 2020
revised: 30 09 2020
accepted: 21 10 2020
pubmed: 15 11 2020
medline: 19 8 2021
entrez: 14 11 2020
Statut: ppublish

Résumé

The purpose of this study was to identify in the EPIRMEX cohort the correlations between MRI brain metrics, including diffuse excessive high signal intensities (DEHSI) obtained with an automated quantitative method and neurodevelopmental outcomes at 2 years. A total of 390 very preterm infants (gestational age at birth≤32 weeks) who underwent brain MRI at term equivalent age at 1.5T (n=338) or 3T (n=52) were prospectively included. Using a validated algorithm, automated metrics of the main brain surfaces (cortical and deep gray matter, white matter, cerebrospinal fluid) and DEHSI with three thresholds were obtained. Linear adjust regressions were performed to assess the correlation between brain metrics with the ages and stages questionnaire (ASQ) score at 2 years. Basal ganglia and thalami, cortex and white matter surfaces positively and significantly correlated with the global ASQ score. For all ASQ sub-domains, basal ganglia and thalami surfaces significantly correlated with the scores. DEHSI was present in 289 premature newborns (74%) without any correlation with the ASQ score. Metrics of DEHSI were greater at 3T than at 1.5T. Brain MRI metrics obtained in our multicentric cohort correlate with the neurodevelopmental outcome at 2 years of age. The quantitative detection of DEHSI is not predictive of adverse outcomes. Our automated algorithm might easily provide useful predictive information in daily practice.

Identifiants

DOI: 10.1016/j.diii.2020.10.009 PMID: 33187906
pubmed: 33187906
pii: S2211-5684(20)30270-9
doi: 10.1016/j.diii.2020.10.009
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

225-232

Informations de copyright

Copyright © 2020 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Auteurs

Baptiste Morel (B)

Pediatric Radiology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France; UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France. Electronic address: bamorel@univ-tours.fr.

Pierre Bertault (P)

UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France.

Géraldine Favrais (G)

Neonatology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France.

Elsa Tavernier (E)

Universities of Tours and Nantes, INSERM 1246-SPHERE, Clinical Investigation Center, INSERM 1415, CHRU de Tours, 37000 Tours, France.

Barthelemy Tosello (B)

Department of Neonatology, North Hospital, APHM University Hospital, 13015 Marseille, France; Aix-Marseille Univ, CNRS, EFS, ADES, 13000 Marseille, France.

Nathalie Bednarek (N)

CReSTIC, Champagne-Ardennes University, EA3804, 51100 Reims, France.

Laurent Barantin (L)

UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France.

Alexandra Chadie (A)

INSERM U1245, Genetics and Pathophysiology of Neurodevelopment Disorders Team, Faculty of Medicine, Institute of Research and Innovation in Biomedicine, Normandy University, 76000 Rouen, France.

Maia Proisy (M)

Department of Radiology, Rennes University Hospital, CHU Hôpital Sud, 35000 Rennes, France.

Yongchao Xu (Y)

School of EIC, Huazhong University of Science and Technology (HUST), Wuhan, China.

Isabelle Bloch (I)

LTCI, Télécom Paris, Institut Polytechnique de Paris, 75013 Paris, France.

Dominique Sirinelli (D)

Pediatric Radiology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France; UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France.

Catherine Adamsbaum (C)

Paris-Sud University, School of Medicine, 94270 Le Kremlin-Bicêtre, France; Assistance-Publique Hopitaux de Paris, Bicetre Hospital, 94270 Le Kremlin-Bicêtre, France.

Clovis Tauber (C)

UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France.

Elie Saliba (E)

UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France; Neonatology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France.
Pediatric Radiology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France; UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France; Neonatology Department, Clocheville Hospital, CHRU of Tours, 37000 Tours, France; Universities of Tours and Nantes, INSERM 1246-SPHERE, Clinical Investigation Center, INSERM 1415, CHRU de Tours, 37000 Tours, France; Department of Neonatology, North Hospital, APHM University Hospital, 13015 Marseille, France; Aix-Marseille Univ, CNRS, EFS, ADES, 13000 Marseille, France; CReSTIC, Champagne-Ardennes University, EA3804, 51100 Reims, France; INSERM U1245, Genetics and Pathophysiology of Neurodevelopment Disorders Team, Faculty of Medicine, Institute of Research and Innovation in Biomedicine, Normandy University, 76000 Rouen, France; Department of Radiology, Rennes University Hospital, CHU Hôpital Sud, 35000 Rennes, France; School of EIC, Huazhong University of Science and Technology (HUST), Wuhan, China; LTCI, Télécom Paris, Institut Polytechnique de Paris, 75013 Paris, France; Paris-Sud University, School of Medicine, 94270 Le Kremlin-Bicêtre, France; Assistance-Publique Hopitaux de Paris, Bicetre Hospital, 94270 Le Kremlin-Bicêtre, France.

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Classifications MeSH

questionsmedicales.fr › Qualité, accès, évaluation des soins de santé › Qualité des soins de santé › Mécanismes d'évaluation des soins de santé › Évaluation de programme › Référenciation › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Personnes › Tranches d'âge › Nourrisson › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Personnes › Tranches d'âge › Nourrisson › Nouveau-né › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Personnes › Tranches d'âge › Nourrisson › Nouveau-né › Prématuré › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Malformations et maladies congénitales, héréditaires et néonatales › Maladies néonatales › Maladies du prématuré › Automated brain MRI metrics in the EPIRMEX...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Automated brain MRI metrics in the EPIRMEX...