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Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study

In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy...

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Autores principales: Cao, Xun, Chen, Xi, Lin, Zhuo-Chen, Liang, Chi-Xiong, Huang, Ying-Ying, Cai, Zhuo-Chen, Li, Jian-Peng, Gao, Ming-Yong, Mai, Hai-Qiang, Li, Chao-Feng, Guo, Xiang, Lyu, Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399485/
https://www.ncbi.nlm.nih.gov/pubmed/36034225
http://dx.doi.org/10.1016/j.isci.2022.104841
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author Cao, Xun
Chen, Xi
Lin, Zhuo-Chen
Liang, Chi-Xiong
Huang, Ying-Ying
Cai, Zhuo-Chen
Li, Jian-Peng
Gao, Ming-Yong
Mai, Hai-Qiang
Li, Chao-Feng
Guo, Xiang
Lyu, Xing
author_facet Cao, Xun
Chen, Xi
Lin, Zhuo-Chen
Liang, Chi-Xiong
Huang, Ying-Ying
Cai, Zhuo-Chen
Li, Jian-Peng
Gao, Ming-Yong
Mai, Hai-Qiang
Li, Chao-Feng
Guo, Xiang
Lyu, Xing
author_sort Cao, Xun
collection PubMed
description In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals.
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spelling pubmed-93994852022-08-25 Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study Cao, Xun Chen, Xi Lin, Zhuo-Chen Liang, Chi-Xiong Huang, Ying-Ying Cai, Zhuo-Chen Li, Jian-Peng Gao, Ming-Yong Mai, Hai-Qiang Li, Chao-Feng Guo, Xiang Lyu, Xing iScience Article In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals. Elsevier 2022-08-03 /pmc/articles/PMC9399485/ /pubmed/36034225 http://dx.doi.org/10.1016/j.isci.2022.104841 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Cao, Xun
Chen, Xi
Lin, Zhuo-Chen
Liang, Chi-Xiong
Huang, Ying-Ying
Cai, Zhuo-Chen
Li, Jian-Peng
Gao, Ming-Yong
Mai, Hai-Qiang
Li, Chao-Feng
Guo, Xiang
Lyu, Xing
Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title_full Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title_fullStr Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title_full_unstemmed Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title_short Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study
title_sort add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on mri: a multicentre, validation study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399485/
https://www.ncbi.nlm.nih.gov/pubmed/36034225
http://dx.doi.org/10.1016/j.isci.2022.104841
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