Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Abstract Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biops...

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Autores principales:	James T. Grist, Stephanie Withey, Christopher Bennett, Heather E. L. Rose, Lesley MacPherson, Adam Oates, Stephen Powell, Jan Novak, Laurence Abernethy, Barry Pizer, Simon Bailey, Steven C. Clifford, Dipayan Mitra, Theodoros N. Arvanitis, Dorothee P. Auer, Shivaram Avula, Richard Grundy, Andrew C. Peet
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/65b300bcf0904636b1d39deb984c2b8c
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spelling	oai:doaj.org-article:65b300bcf0904636b1d39deb984c2b8c2021-12-02T18:13:52ZCombining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors10.1038/s41598-021-96189-82045-2322https://doaj.org/article/65b300bcf0904636b1d39deb984c2b8c2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96189-8https://doaj.org/toc/2045-2322Abstract Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.James T. GristStephanie WitheyChristopher BennettHeather E. L. RoseLesley MacPhersonAdam OatesStephen PowellJan NovakLaurence AbernethyBarry PizerSimon BaileySteven C. CliffordDipayan MitraTheodoros N. ArvanitisDorothee P. AuerShivaram AvulaRichard GrundyAndrew C. PeetNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q James T. Grist Stephanie Withey Christopher Bennett Heather E. L. Rose Lesley MacPherson Adam Oates Stephen Powell Jan Novak Laurence Abernethy Barry Pizer Simon Bailey Steven C. Clifford Dipayan Mitra Theodoros N. Arvanitis Dorothee P. Auer Shivaram Avula Richard Grundy Andrew C. Peet Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
description	Abstract Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.
format	article
author	James T. Grist Stephanie Withey Christopher Bennett Heather E. L. Rose Lesley MacPherson Adam Oates Stephen Powell Jan Novak Laurence Abernethy Barry Pizer Simon Bailey Steven C. Clifford Dipayan Mitra Theodoros N. Arvanitis Dorothee P. Auer Shivaram Avula Richard Grundy Andrew C. Peet
author_facet	James T. Grist Stephanie Withey Christopher Bennett Heather E. L. Rose Lesley MacPherson Adam Oates Stephen Powell Jan Novak Laurence Abernethy Barry Pizer Simon Bailey Steven C. Clifford Dipayan Mitra Theodoros N. Arvanitis Dorothee P. Auer Shivaram Avula Richard Grundy Andrew C. Peet
author_sort	James T. Grist
title	Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
title_short	Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
title_full	Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
title_fullStr	Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
title_full_unstemmed	Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
title_sort	combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/65b300bcf0904636b1d39deb984c2b8c
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Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

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