Cargando…
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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784772532312735744 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9399485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT caoxun addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT chenxi addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT linzhuochen addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT liangchixiong addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT huangyingying addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT caizhuochen addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT lijianpeng addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT gaomingyong addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT maihaiqiang addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT lichaofeng addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT guoxiang addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy AT lyuxing addonindividualizingpredictionofnasopharyngealcarcinomausingdeeplearningbasedonmriamulticentrevalidationstudy |