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基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often bas...

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Detalles Bibliográficos
Autores principales: QIN, Kai, FU, Xiaolong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial board of Chinese Journal of Lung Cancer 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987091/
https://www.ncbi.nlm.nih.gov/pubmed/36792078
http://dx.doi.org/10.3779/j.issn.1009-3419.2023.101.01
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author QIN, Kai
FU, Xiaolong
author_facet QIN, Kai
FU, Xiaolong
author_sort QIN, Kai
collection PubMed
description Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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spelling pubmed-99870912023-03-07 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展 QIN, Kai FU, Xiaolong Zhongguo Fei Ai Za Zhi Review Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice. Editorial board of Chinese Journal of Lung Cancer 2023-01-20 /pmc/articles/PMC9987091/ /pubmed/36792078 http://dx.doi.org/10.3779/j.issn.1009-3419.2023.101.01 Text en https://creativecommons.org/licenses/by/3.0/This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 3.0) License. See: https://creativecommons.org/licenses/by/3.0/.
spellingShingle Review
QIN, Kai
FU, Xiaolong
基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title_full 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title_fullStr 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title_full_unstemmed 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title_short 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
title_sort 基于影像学诊断非小细胞肺癌肿大淋巴结良恶性的研究进展
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987091/
https://www.ncbi.nlm.nih.gov/pubmed/36792078
http://dx.doi.org/10.3779/j.issn.1009-3419.2023.101.01
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