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Radiomics-based T-staging of hollow organ cancers

Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth character...

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Autores principales: Huang, Dong, Xu, Xiaopan, Du, Peng, Feng, Yuefei, Zhang, Xi, Lu, Hongbing, Liu, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499612/
https://www.ncbi.nlm.nih.gov/pubmed/37719013
http://dx.doi.org/10.3389/fonc.2023.1191519
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author Huang, Dong
Xu, Xiaopan
Du, Peng
Feng, Yuefei
Zhang, Xi
Lu, Hongbing
Liu, Yang
author_facet Huang, Dong
Xu, Xiaopan
Du, Peng
Feng, Yuefei
Zhang, Xi
Lu, Hongbing
Liu, Yang
author_sort Huang, Dong
collection PubMed
description Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.
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spelling pubmed-104996122023-09-15 Radiomics-based T-staging of hollow organ cancers Huang, Dong Xu, Xiaopan Du, Peng Feng, Yuefei Zhang, Xi Lu, Hongbing Liu, Yang Front Oncol Oncology Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm. Frontiers Media S.A. 2023-08-30 /pmc/articles/PMC10499612/ /pubmed/37719013 http://dx.doi.org/10.3389/fonc.2023.1191519 Text en Copyright © 2023 Huang, Xu, Du, Feng, Zhang, Lu and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Huang, Dong
Xu, Xiaopan
Du, Peng
Feng, Yuefei
Zhang, Xi
Lu, Hongbing
Liu, Yang
Radiomics-based T-staging of hollow organ cancers
title Radiomics-based T-staging of hollow organ cancers
title_full Radiomics-based T-staging of hollow organ cancers
title_fullStr Radiomics-based T-staging of hollow organ cancers
title_full_unstemmed Radiomics-based T-staging of hollow organ cancers
title_short Radiomics-based T-staging of hollow organ cancers
title_sort radiomics-based t-staging of hollow organ cancers
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499612/
https://www.ncbi.nlm.nih.gov/pubmed/37719013
http://dx.doi.org/10.3389/fonc.2023.1191519
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