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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...

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Detalles Bibliográficos
Autores principales: Sun, Shawn, Besson, Florent L., Zhao, Binsheng, Schwartz, Lawrence H., Dercle, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2020
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.
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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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