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Article: Review on applications of metastatic lymph node based radiomic assessment in nasopharyngeal carcinoma

TitleReview on applications of metastatic lymph node based radiomic assessment in nasopharyngeal carcinoma
Authors
Keywordsartificial intelligence
cervical lymphadenopathy
deep learning
head and neck oncology
Nasopharyngeal carcinoma
NPC nodal metastasis
radiomics
review
Issue Date28-Apr-2023
PublisherOAE Publishing
Citation
Journal of Cancer Metastasis and Treatment, 2023, v. 9 How to Cite?
Abstract

Nasopharyngeal carcinoma (NPC) has a distinct geographical prevalence in Southern China and Southeast Asia with a high overall survival rate (> 90%) in the early stage of the disease. However, almost 85% of patients suffer from the locally advanced disease with nodal metastasis at diagnosis. The overall survival rate would drastically drop to 63%. In addition to the generic tumor, nodal, and metastasis (TNM) staging, radiomic studies focusing on primary nasopharyngeal tumors have gained attention in precision medicine with artificial intelligence. While the heterogeneous presentation of cervical lymphadenopathy in locally advanced NPC is regarded as the same clinical stage under TNM criteria, radiomic analysis provides more insights into risk stratification, treatment differentiation, and survival prediction. There appears to be a lack of a review that consolidates radiomics-related studies on lymph node metastasis in NPC. The aim of this paper is to summarize the state-of-the-art of radiomics for lymph node analysis in NPC, including its potential use in prognostic prediction, treatment response, and overall survival for this cohort of patients.


Persistent Identifierhttp://hdl.handle.net/10722/337959
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChan, PL-
dc.contributor.authorLeung, WS-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorLee, SW-
dc.contributor.authorChan, JY-
dc.date.accessioned2024-03-11T10:25:13Z-
dc.date.available2024-03-11T10:25:13Z-
dc.date.issued2023-04-28-
dc.identifier.citationJournal of Cancer Metastasis and Treatment, 2023, v. 9-
dc.identifier.issn2394-4722-
dc.identifier.urihttp://hdl.handle.net/10722/337959-
dc.description.abstract<p>Nasopharyngeal carcinoma (NPC) has a distinct geographical prevalence in Southern China and Southeast Asia with a high overall survival rate (> 90%) in the early stage of the disease. However, almost 85% of patients suffer from the locally advanced disease with nodal metastasis at diagnosis. The overall survival rate would drastically drop to 63%. In addition to the generic tumor, nodal, and metastasis (TNM) staging, radiomic studies focusing on primary nasopharyngeal tumors have gained attention in precision medicine with artificial intelligence. While the heterogeneous presentation of cervical lymphadenopathy in locally advanced NPC is regarded as the same clinical stage under TNM criteria, radiomic analysis provides more insights into risk stratification, treatment differentiation, and survival prediction. There appears to be a lack of a review that consolidates radiomics-related studies on lymph node metastasis in NPC. The aim of this paper is to summarize the state-of-the-art of radiomics for lymph node analysis in NPC, including its potential use in prognostic prediction, treatment response, and overall survival for this cohort of patients.<br></p>-
dc.languageeng-
dc.publisherOAE Publishing-
dc.relation.ispartofJournal of Cancer Metastasis and Treatment-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectcervical lymphadenopathy-
dc.subjectdeep learning-
dc.subjecthead and neck oncology-
dc.subjectNasopharyngeal carcinoma-
dc.subjectNPC nodal metastasis-
dc.subjectradiomics-
dc.subjectreview-
dc.titleReview on applications of metastatic lymph node based radiomic assessment in nasopharyngeal carcinoma-
dc.typeArticle-
dc.identifier.doi10.20517/2394-4722.2022.100-
dc.identifier.scopuseid_2-s2.0-85165416373-
dc.identifier.volume9-
dc.identifier.eissn2454-2857-
dc.identifier.issnl2394-4722-

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