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Toward radiomics for assessment of response to systemic therapies in lung cancer
This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771714/ https://www.ncbi.nlm.nih.gov/pubmed/33473253 http://dx.doi.org/10.18632/oncotarget.27847 |
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author | Sun, Shawn Besson, Florent L. Zhao, Binsheng Schwartz, Lawrence H. Dercle, Laurent |
author_facet | Sun, Shawn Besson, Florent L. Zhao, Binsheng Schwartz, Lawrence H. Dercle, Laurent |
author_sort | Sun, Shawn |
collection | PubMed |
description | This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine. |
format | Online Article Text |
id | pubmed-7771714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-77717142021-01-19 Toward radiomics for assessment of response to systemic therapies in lung cancer Sun, Shawn Besson, Florent L. Zhao, Binsheng Schwartz, Lawrence H. Dercle, Laurent Oncotarget Research Perspective This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine. Impact Journals LLC 2020-12-22 /pmc/articles/PMC7771714/ /pubmed/33473253 http://dx.doi.org/10.18632/oncotarget.27847 Text en Copyright: © 2020 Sun et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Perspective Sun, Shawn Besson, Florent L. Zhao, Binsheng Schwartz, Lawrence H. Dercle, Laurent Toward radiomics for assessment of response to systemic therapies in lung cancer |
title | Toward radiomics for assessment of response to systemic therapies in lung cancer |
title_full | Toward radiomics for assessment of response to systemic therapies in lung cancer |
title_fullStr | Toward radiomics for assessment of response to systemic therapies in lung cancer |
title_full_unstemmed | Toward radiomics for assessment of response to systemic therapies in lung cancer |
title_short | Toward radiomics for assessment of response to systemic therapies in lung cancer |
title_sort | toward radiomics for assessment of response to systemic therapies in lung cancer |
topic | Research Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771714/ https://www.ncbi.nlm.nih.gov/pubmed/33473253 http://dx.doi.org/10.18632/oncotarget.27847 |
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