Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

The ratio of tumour area to metastatic lymph node area (T/MLN) is a clinical indicator that can improve prognosis prediction of gastric cancer. Here, the authors use machine learning on whole slide images to generate a method that can predict metastatic lymph nodes and obtain T/MLN.

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Autores principales:	Xiaodong Wang, Ying Chen, Yunshu Gao, Huiqing Zhang, Zehui Guan, Zhou Dong, Yuxuan Zheng, Jiarui Jiang, Haoqing Yang, Liming Wang, Xianming Huang, Lirong Ai, Wenlong Yu, Hongwei Li, Changsheng Dong, Zhou Zhou, Xiyang Liu, Guanzhen Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/21cb9a0523de44f7b48e8fed3c596529
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:21cb9a0523de44f7b48e8fed3c5965292021-12-02T13:30:36ZPredicting gastric cancer outcome from resected lymph node histopathology images using deep learning10.1038/s41467-021-21674-72041-1723https://doaj.org/article/21cb9a0523de44f7b48e8fed3c5965292021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21674-7https://doaj.org/toc/2041-1723The ratio of tumour area to metastatic lymph node area (T/MLN) is a clinical indicator that can improve prognosis prediction of gastric cancer. Here, the authors use machine learning on whole slide images to generate a method that can predict metastatic lymph nodes and obtain T/MLN.Xiaodong WangYing ChenYunshu GaoHuiqing ZhangZehui GuanZhou DongYuxuan ZhengJiarui JiangHaoqing YangLiming WangXianming HuangLirong AiWenlong YuHongwei LiChangsheng DongZhou ZhouXiyang LiuGuanzhen YuNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xiaodong Wang Ying Chen Yunshu Gao Huiqing Zhang Zehui Guan Zhou Dong Yuxuan Zheng Jiarui Jiang Haoqing Yang Liming Wang Xianming Huang Lirong Ai Wenlong Yu Hongwei Li Changsheng Dong Zhou Zhou Xiyang Liu Guanzhen Yu Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
description	The ratio of tumour area to metastatic lymph node area (T/MLN) is a clinical indicator that can improve prognosis prediction of gastric cancer. Here, the authors use machine learning on whole slide images to generate a method that can predict metastatic lymph nodes and obtain T/MLN.
format	article
author	Xiaodong Wang Ying Chen Yunshu Gao Huiqing Zhang Zehui Guan Zhou Dong Yuxuan Zheng Jiarui Jiang Haoqing Yang Liming Wang Xianming Huang Lirong Ai Wenlong Yu Hongwei Li Changsheng Dong Zhou Zhou Xiyang Liu Guanzhen Yu
author_facet	Xiaodong Wang Ying Chen Yunshu Gao Huiqing Zhang Zehui Guan Zhou Dong Yuxuan Zheng Jiarui Jiang Haoqing Yang Liming Wang Xianming Huang Lirong Ai Wenlong Yu Hongwei Li Changsheng Dong Zhou Zhou Xiyang Liu Guanzhen Yu
author_sort	Xiaodong Wang
title	Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
title_short	Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
title_full	Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
title_fullStr	Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
title_full_unstemmed	Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
title_sort	predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/21cb9a0523de44f7b48e8fed3c596529
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Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

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