Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images

Abstract This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2–3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86)...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Pim Moeskops, Ivana Išgum, Kristin Keunen, Nathalie H. P. Claessens, Ingrid C. van Haastert, Floris Groenendaal, Linda S. de Vries, Max A. Viergever, Manon J. N. L. Benders
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5769d66f22a044ac8daa040d56f38ab1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5769d66f22a044ac8daa040d56f38ab1
record_format	dspace
spelling	oai:doaj.org-article:5769d66f22a044ac8daa040d56f38ab12021-12-02T16:07:56ZPrediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images10.1038/s41598-017-02307-w2045-2322https://doaj.org/article/5769d66f22a044ac8daa040d56f38ab12017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02307-whttps://doaj.org/toc/2045-2322Abstract This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2–3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86) and 40 weeks PMA (n = 153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (<85) at 2–3 years chronological age as assessed by the Bayley scales. Based on the images acquired at 30 weeks PMA, the automatic identification resulted in an area under the receiver operation characteristic curve (AUC) of 0.78 for low cognitive outcome, and an AUC of 0.80 for low motor outcome. Identification based on the change of the descriptors between 30 and 40 weeks PMA (n = 66) resulted in an AUC of 0.80 for low cognitive outcome and an AUC of 0.85 for low motor outcome. This study provides evidence of the feasibility of identification of preterm infants at risk of cognitive and motor impairments based on descriptors automatically computed from images acquired at 30 and 40 weeks PMA.Pim MoeskopsIvana IšgumKristin KeunenNathalie H. P. ClaessensIngrid C. van HaastertFloris GroenendaalLinda S. de VriesMax A. ViergeverManon J. N. L. BendersNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pim Moeskops Ivana Išgum Kristin Keunen Nathalie H. P. Claessens Ingrid C. van Haastert Floris Groenendaal Linda S. de Vries Max A. Viergever Manon J. N. L. Benders Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
description	Abstract This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2–3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86) and 40 weeks PMA (n = 153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (<85) at 2–3 years chronological age as assessed by the Bayley scales. Based on the images acquired at 30 weeks PMA, the automatic identification resulted in an area under the receiver operation characteristic curve (AUC) of 0.78 for low cognitive outcome, and an AUC of 0.80 for low motor outcome. Identification based on the change of the descriptors between 30 and 40 weeks PMA (n = 66) resulted in an AUC of 0.80 for low cognitive outcome and an AUC of 0.85 for low motor outcome. This study provides evidence of the feasibility of identification of preterm infants at risk of cognitive and motor impairments based on descriptors automatically computed from images acquired at 30 and 40 weeks PMA.
format	article
author	Pim Moeskops Ivana Išgum Kristin Keunen Nathalie H. P. Claessens Ingrid C. van Haastert Floris Groenendaal Linda S. de Vries Max A. Viergever Manon J. N. L. Benders
author_facet	Pim Moeskops Ivana Išgum Kristin Keunen Nathalie H. P. Claessens Ingrid C. van Haastert Floris Groenendaal Linda S. de Vries Max A. Viergever Manon J. N. L. Benders
author_sort	Pim Moeskops
title	Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
title_short	Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
title_full	Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
title_fullStr	Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
title_full_unstemmed	Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images
title_sort	prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal mr brain images
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/5769d66f22a044ac8daa040d56f38ab1
work_keys_str_mv	AT pimmoeskops predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT ivanaisgum predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT kristinkeunen predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT nathaliehpclaessens predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT ingridcvanhaastert predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT florisgroenendaal predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT lindasdevries predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT maxaviergever predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages AT manonjnlbenders predictionofcognitiveandmotoroutcomeofpreterminfantsbasedonautomaticquantitativedescriptorsfromneonatalmrbrainimages
_version_	1718384656210984960

Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images

Ejemplares similares