Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging

Evaluation of tumor response to antivascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier or be more critical than tumor size change. Here, the authors present an analysis utilizing a deep learning netwo...

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Autores principales:	Lin Lu, Laurent Dercle, Binsheng Zhao, Lawrence H. Schwartz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/05663aaff3704c2994241e2f448faf34
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spelling	oai:doaj.org-article:05663aaff3704c2994241e2f448faf342021-11-21T12:35:22ZDeep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging10.1038/s41467-021-26990-62041-1723https://doaj.org/article/05663aaff3704c2994241e2f448faf342021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26990-6https://doaj.org/toc/2041-1723Evaluation of tumor response to antivascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier or be more critical than tumor size change. Here, the authors present an analysis utilizing a deep learning network to characterize tumor morphological change as well as tumor size changes for response assessment in mCRC patients.Lin LuLaurent DercleBinsheng ZhaoLawrence H. SchwartzNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Lin Lu Laurent Dercle Binsheng Zhao Lawrence H. Schwartz Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
description	Evaluation of tumor response to antivascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier or be more critical than tumor size change. Here, the authors present an analysis utilizing a deep learning network to characterize tumor morphological change as well as tumor size changes for response assessment in mCRC patients.
format	article
author	Lin Lu Laurent Dercle Binsheng Zhao Lawrence H. Schwartz
author_facet	Lin Lu Laurent Dercle Binsheng Zhao Lawrence H. Schwartz
author_sort	Lin Lu
title	Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
title_short	Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
title_full	Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
title_fullStr	Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
title_full_unstemmed	Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
title_sort	deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05663aaff3704c2994241e2f448faf34
work_keys_str_mv	AT linlu deeplearningforthepredictionofearlyontreatmentresponseinmetastaticcolorectalcancerfromserialmedicalimaging AT laurentdercle deeplearningforthepredictionofearlyontreatmentresponseinmetastaticcolorectalcancerfromserialmedicalimaging AT binshengzhao deeplearningforthepredictionofearlyontreatmentresponseinmetastaticcolorectalcancerfromserialmedicalimaging AT lawrencehschwartz deeplearningforthepredictionofearlyontreatmentresponseinmetastaticcolorectalcancerfromserialmedicalimaging
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Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging

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