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

<h4>Background</h4>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.<h4>Methods</h4>Ultrasound and MRI were performed on 91 SGA fetuses at 37 wee...

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Autores principales:	Magdalena Sanz-Cortes, Giuseppe A Ratta, Francesc Figueras, Elisenda Bonet-Carne, Nelly Padilla, Angela Arranz, Nuria Bargallo, Eduard Gratacos
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f81075bc142f47ebb9e7b8c0fb2486bc
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spelling	oai:doaj.org-article:f81075bc142f47ebb9e7b8c0fb2486bc2021-11-18T09:02:44ZAutomatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior.1932-620310.1371/journal.pone.0069595https://doaj.org/article/f81075bc142f47ebb9e7b8c0fb2486bc2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23922750/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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 <5(th) centile and as normal if all areas were >5(th) centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm.<h4>Results</h4>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.<h4>Conclusions</h4>Fetal brain MRI textural patterns were associated with neonatal neurodevelopment. Brain MRI TA could be a useful tool to predict abnormal neurodevelopment in SGA.Magdalena Sanz-CortesGiuseppe A RattaFrancesc FiguerasElisenda Bonet-CarneNelly PadillaAngela ArranzNuria BargalloEduard GratacosPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69595 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Magdalena Sanz-Cortes Giuseppe A Ratta Francesc Figueras Elisenda Bonet-Carne Nelly Padilla Angela Arranz Nuria Bargallo Eduard Gratacos Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior.
description	<h4>Background</h4>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.<h4>Methods</h4>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 <5(th) centile and as normal if all areas were >5(th) centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm.<h4>Results</h4>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.<h4>Conclusions</h4>Fetal brain MRI textural patterns were associated with neonatal neurodevelopment. Brain MRI TA could be a useful tool to predict abnormal neurodevelopment in SGA.
format	article
author	Magdalena Sanz-Cortes Giuseppe A Ratta Francesc Figueras Elisenda Bonet-Carne Nelly Padilla Angela Arranz Nuria Bargallo Eduard Gratacos
author_facet	Magdalena Sanz-Cortes Giuseppe A Ratta Francesc Figueras Elisenda Bonet-Carne Nelly Padilla Angela Arranz Nuria Bargallo Eduard Gratacos
author_sort	Magdalena Sanz-Cortes
title	Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior.
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.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/f81075bc142f47ebb9e7b8c0fb2486bc
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Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior.

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