Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features

Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology sig...

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Detalles Bibliográficos
Autores principales: Zhang, Fang, Yao, Su, Li, Zhi, Liang, Changhong, Zhao, Ke, Huang, Yanqi, Gao, Ying, Qu, Jinrong, Li, Zhenhui, Liu, Zaiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Short Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403709/
https://www.ncbi.nlm.nih.gov/pubmed/32594660
http://dx.doi.org/10.1002/ctm2.110
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author Zhang, Fang
Yao, Su
Li, Zhi
Liang, Changhong
Zhao, Ke
Huang, Yanqi
Gao, Ying
Qu, Jinrong
Li, Zhenhui
Liu, Zaiyi
author_facet Zhang, Fang
Yao, Su
Li, Zhi
Liang, Changhong
Zhao, Ke
Huang, Yanqi
Gao, Ying
Qu, Jinrong
Li, Zhenhui
Liu, Zaiyi
author_sort Zhang, Fang
collection PubMed
description Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology signature is an independent predictor of treatment response to nCRT in LARC [Image: see text]
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spelling pubmed-74037092020-08-06 Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features Zhang, Fang Yao, Su Li, Zhi Liang, Changhong Zhao, Ke Huang, Yanqi Gao, Ying Qu, Jinrong Li, Zhenhui Liu, Zaiyi Clin Transl Med Short Communication Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology signature is an independent predictor of treatment response to nCRT in LARC [Image: see text] John Wiley and Sons Inc. 2020-06-28 /pmc/articles/PMC7403709/ /pubmed/32594660 http://dx.doi.org/10.1002/ctm2.110 Text en © 2020 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Short Communication
Zhang, Fang
Yao, Su
Li, Zhi
Liang, Changhong
Zhao, Ke
Huang, Yanqi
Gao, Ying
Qu, Jinrong
Li, Zhenhui
Liu, Zaiyi
Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title_full Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title_fullStr Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title_full_unstemmed Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title_short Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
title_sort predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403709/
https://www.ncbi.nlm.nih.gov/pubmed/32594660
http://dx.doi.org/10.1002/ctm2.110
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