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Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors
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-confirm...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460620/ https://www.ncbi.nlm.nih.gov/pubmed/34556677 http://dx.doi.org/10.1038/s41598-021-96189-8 |
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author | Grist, James T. Withey, Stephanie Bennett, Christopher Rose, Heather E. L. MacPherson, Lesley Oates, Adam Powell, Stephen Novak, Jan Abernethy, Laurence Pizer, Barry Bailey, Simon Clifford, Steven C. Mitra, Dipayan Arvanitis, Theodoros N. Auer, Dorothee P. Avula, Shivaram Grundy, Richard Peet, Andrew C. |
author_facet | Grist, James T. Withey, Stephanie Bennett, Christopher Rose, Heather E. L. MacPherson, Lesley Oates, Adam Powell, Stephen Novak, Jan Abernethy, Laurence Pizer, Barry Bailey, Simon Clifford, Steven C. Mitra, Dipayan Arvanitis, Theodoros N. Auer, Dorothee P. Avula, Shivaram Grundy, Richard Peet, Andrew C. |
author_sort | Grist, James T. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8460620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84606202021-09-24 Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors Grist, James T. Withey, Stephanie Bennett, Christopher Rose, Heather E. L. MacPherson, Lesley Oates, Adam Powell, Stephen Novak, Jan Abernethy, Laurence Pizer, Barry Bailey, Simon Clifford, Steven C. Mitra, Dipayan Arvanitis, Theodoros N. Auer, Dorothee P. Avula, Shivaram Grundy, Richard Peet, Andrew C. Sci Rep Article 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. Nature Publishing Group UK 2021-09-23 /pmc/articles/PMC8460620/ /pubmed/34556677 http://dx.doi.org/10.1038/s41598-021-96189-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Grist, James T. Withey, Stephanie Bennett, Christopher Rose, Heather E. L. MacPherson, Lesley Oates, Adam Powell, Stephen Novak, Jan Abernethy, Laurence Pizer, Barry Bailey, Simon Clifford, Steven C. Mitra, Dipayan Arvanitis, Theodoros N. Auer, Dorothee P. Avula, Shivaram Grundy, Richard Peet, Andrew C. Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460620/ https://www.ncbi.nlm.nih.gov/pubmed/34556677 http://dx.doi.org/10.1038/s41598-021-96189-8 |
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