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Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality...

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Detalles Bibliográficos
Autores principales: Feng, Mary, Valdes, Gilmer, Dixit, Nayha, Solberg, Timothy D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913324/
https://www.ncbi.nlm.nih.gov/pubmed/29719815
http://dx.doi.org/10.3389/fonc.2018.00110
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author Feng, Mary
Valdes, Gilmer
Dixit, Nayha
Solberg, Timothy D.
author_facet Feng, Mary
Valdes, Gilmer
Dixit, Nayha
Solberg, Timothy D.
author_sort Feng, Mary
collection PubMed
description Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
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spelling pubmed-59133242018-05-01 Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs Feng, Mary Valdes, Gilmer Dixit, Nayha Solberg, Timothy D. Front Oncol Oncology Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner. Frontiers Media S.A. 2018-04-17 /pmc/articles/PMC5913324/ /pubmed/29719815 http://dx.doi.org/10.3389/fonc.2018.00110 Text en Copyright © 2018 Feng, Valdes, Dixit and Solberg. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Feng, Mary
Valdes, Gilmer
Dixit, Nayha
Solberg, Timothy D.
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title_full Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title_fullStr Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title_full_unstemmed Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title_short Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
title_sort machine learning in radiation oncology: opportunities, requirements, and needs
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913324/
https://www.ncbi.nlm.nih.gov/pubmed/29719815
http://dx.doi.org/10.3389/fonc.2018.00110
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