Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenari...

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Main Authors:	Jinhua Yu, Yinhui Deng, Tongtong Liu, Jin Zhou, Xiaohong Jia, Tianlei Xiao, Shichong Zhou, Jiawei Li, Yi Guo, Yuanyuan Wang, Jianqiao Zhou, Cai Chang
Format:	article
Language:	EN
Published:	Nature Portfolio 2020
Subjects:	Science Q
Online Access:	https://doaj.org/article/48f1569b05a54610bcc9823ee3281839
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spelling	oai:doaj.org-article:48f1569b05a54610bcc9823ee32818392021-12-02T15:14:48ZLymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics10.1038/s41467-020-18497-32041-1723https://doaj.org/article/48f1569b05a54610bcc9823ee32818392020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18497-3https://doaj.org/toc/2041-1723A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.Jinhua YuYinhui DengTongtong LiuJin ZhouXiaohong JiaTianlei XiaoShichong ZhouJiawei LiYi GuoYuanyuan WangJianqiao ZhouCai ChangNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jinhua Yu Yinhui Deng Tongtong Liu Jin Zhou Xiaohong Jia Tianlei Xiao Shichong Zhou Jiawei Li Yi Guo Yuanyuan Wang Jianqiao Zhou Cai Chang Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
description	A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.
format	article
author	Jinhua Yu Yinhui Deng Tongtong Liu Jin Zhou Xiaohong Jia Tianlei Xiao Shichong Zhou Jiawei Li Yi Guo Yuanyuan Wang Jianqiao Zhou Cai Chang
author_facet	Jinhua Yu Yinhui Deng Tongtong Liu Jin Zhou Xiaohong Jia Tianlei Xiao Shichong Zhou Jiawei Li Yi Guo Yuanyuan Wang Jianqiao Zhou Cai Chang
author_sort	Jinhua Yu
title	Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
title_short	Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
title_full	Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
title_fullStr	Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
title_full_unstemmed	Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
title_sort	lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/48f1569b05a54610bcc9823ee3281839
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_version_	1718387565956956160

Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

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