Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer

Abstract To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test group at a ratio of 7:3. The model for lymph...

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Autores principales:	Cheng-Mao Zhou, Ying Wang, Hao-Tian Ye, Shuping Yan, Muhuo Ji, Panmiao Liu, Jian-Jun Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b60c9cebea0d48e682d873e771fbfc51
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spelling	oai:doaj.org-article:b60c9cebea0d48e682d873e771fbfc512021-12-02T14:01:35ZMachine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer10.1038/s41598-020-80582-w2045-2322https://doaj.org/article/b60c9cebea0d48e682d873e771fbfc512021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80582-whttps://doaj.org/toc/2045-2322Abstract To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test group at a ratio of 7:3. The model for lymph node metastasis was established with python machine learning. The Gbdt algorithm in the machine learning results finds that number of resected nodes, lymphovascular invasion and tumor size are the primary 3 factors that account for the weight of LNM. Effect of the LNM model of PDC gastric cancer patients in the training group: Among the 7 algorithm models, the highest accuracy rate was that of GBDT (0.955); The AUC values for the 7 algorithms were, from high to low, XGB (0.881), RF (0.802), GBDT (0.798), LR (0.778), XGB + LR (0.739), RF + LR (0.691) and GBDT + LR (0.626). Results of the LNM model of PDC gastric cancer patients in test group : Among the 7 algorithmic models, XGB had the highest accuracy rate (0.952); Among the 7 algorithms, the AUC values, from high to low, were GBDT (0.788), RF (0.765), XGB (0.762), LR (0.750), RF + LR (0.678), GBDT + LR (0.650) and XGB + LR (0.619). Single machine learning algorithm can predict LNM in poorly differentiated-type intramucosal gastric cancer, but fusion algorithm can not improve the effect of machine learning in predicting LNM.Cheng-Mao ZhouYing WangHao-Tian YeShuping YanMuhuo JiPanmiao LiuJian-Jun YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cheng-Mao Zhou Ying Wang Hao-Tian Ye Shuping Yan Muhuo Ji Panmiao Liu Jian-Jun Yang Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
description	Abstract To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test group at a ratio of 7:3. The model for lymph node metastasis was established with python machine learning. The Gbdt algorithm in the machine learning results finds that number of resected nodes, lymphovascular invasion and tumor size are the primary 3 factors that account for the weight of LNM. Effect of the LNM model of PDC gastric cancer patients in the training group: Among the 7 algorithm models, the highest accuracy rate was that of GBDT (0.955); The AUC values for the 7 algorithms were, from high to low, XGB (0.881), RF (0.802), GBDT (0.798), LR (0.778), XGB + LR (0.739), RF + LR (0.691) and GBDT + LR (0.626). Results of the LNM model of PDC gastric cancer patients in test group : Among the 7 algorithmic models, XGB had the highest accuracy rate (0.952); Among the 7 algorithms, the AUC values, from high to low, were GBDT (0.788), RF (0.765), XGB (0.762), LR (0.750), RF + LR (0.678), GBDT + LR (0.650) and XGB + LR (0.619). Single machine learning algorithm can predict LNM in poorly differentiated-type intramucosal gastric cancer, but fusion algorithm can not improve the effect of machine learning in predicting LNM.
format	article
author	Cheng-Mao Zhou Ying Wang Hao-Tian Ye Shuping Yan Muhuo Ji Panmiao Liu Jian-Jun Yang
author_facet	Cheng-Mao Zhou Ying Wang Hao-Tian Ye Shuping Yan Muhuo Ji Panmiao Liu Jian-Jun Yang
author_sort	Cheng-Mao Zhou
title	Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
title_short	Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
title_full	Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
title_fullStr	Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
title_full_unstemmed	Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
title_sort	machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b60c9cebea0d48e682d873e771fbfc51
work_keys_str_mv	AT chengmaozhou machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT yingwang machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT haotianye machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT shupingyan machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT muhuoji machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT panmiaoliu machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer AT jianjunyang machinelearningpredictslymphnodemetastasisofpoorlydifferentiatedtypeintramucosalgastriccancer
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Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer

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