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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Editorial board of Chinese Journal of Lung Cancer
2023
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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. |
format | Online Article Text |
id | pubmed-9987091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Editorial board of Chinese Journal of Lung Cancer |
record_format | MEDLINE/PubMed |
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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