Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior

Background: We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods: Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia...

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Detalles Bibliográficos
Autores: Sanz Cortés, Magdalena, Ratta, Giuseppe A., Figueras Retuerta, Francesc, Bonet Carné, Elisenda, Padilla Gomes, Nelly, Arranz Betegón, Ángela, Bargalló Alabart, Núria, Gratacós Solsona, Eduard
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/121872
Acceso en línea:https://hdl.handle.net/2445/121872
Access Level:acceso abierto
Palabra clave:Neonatologia
Neurociències
Neonatology
Neurosciences
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spelling Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehaviorSanz Cortés, MagdalenaRatta, Giuseppe A.Figueras Retuerta, FrancescBonet Carné, ElisendaPadilla Gomes, NellyArranz Betegón, ÁngelaBargalló Alabart, NúriaGratacós Solsona, EduardNeonatologiaNeurociènciesNeonatologyNeurosciencesBackground: We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods: Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if $1 area was ,5th centile and as normal if all areas were .5th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm. Results: Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum. Conclusions: Fetal brain MRI textural patterns were associatedPublic Library of Science (PLoS)2018201820132018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion7 p.application/pdfhttps://hdl.handle.net/2445/121872Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1371/journal.pone.0069595PLoS One, 2013, vol. 8, num. 7, p. e69595https://doi.org/10.1371/journal.pone.0069595cc-by (c) Sanz-Cortes et al., 2013http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1218722026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
title Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
spellingShingle Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
Sanz Cortés, Magdalena
Neonatologia
Neurociències
Neonatology
Neurosciences
title_short Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
title_full Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
title_fullStr Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
title_full_unstemmed Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
title_sort Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
dc.creator.none.fl_str_mv Sanz Cortés, Magdalena
Ratta, Giuseppe A.
Figueras Retuerta, Francesc
Bonet Carné, Elisenda
Padilla Gomes, Nelly
Arranz Betegón, Ángela
Bargalló Alabart, Núria
Gratacós Solsona, Eduard
author Sanz Cortés, Magdalena
author_facet Sanz Cortés, Magdalena
Ratta, Giuseppe A.
Figueras Retuerta, Francesc
Bonet Carné, Elisenda
Padilla Gomes, Nelly
Arranz Betegón, Ángela
Bargalló Alabart, Núria
Gratacós Solsona, Eduard
author_role author
author2 Ratta, Giuseppe A.
Figueras Retuerta, Francesc
Bonet Carné, Elisenda
Padilla Gomes, Nelly
Arranz Betegón, Ángela
Bargalló Alabart, Núria
Gratacós Solsona, Eduard
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Neonatologia
Neurociències
Neonatology
Neurosciences
topic Neonatologia
Neurociències
Neonatology
Neurosciences
description Background: We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods: Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if $1 area was ,5th centile and as normal if all areas were .5th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm. Results: Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum. Conclusions: Fetal brain MRI textural patterns were associated
publishDate 2013
dc.date.none.fl_str_mv 2013
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/121872
url https://hdl.handle.net/2445/121872
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0069595
PLoS One, 2013, vol. 8, num. 7, p. e69595
https://doi.org/10.1371/journal.pone.0069595
dc.rights.none.fl_str_mv cc-by (c) Sanz-Cortes et al., 2013
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Sanz-Cortes et al., 2013
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 7 p.
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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