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Review
. 2023 Jan 20;26(1):31-37.
doi: 10.3779/j.issn.1009-3419.2023.101.01.

[Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer]

[Article in Chinese]
Kai Qin  1 , Xiaolong Fu  1
Affiliations
Review

[Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer]

[Article in Chinese]
Kai Qin et al. Zhongguo Fei Ai Za Zhi. .

Abstract
in English, Chinese

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. .

【中文题目：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】 【中文摘要：非小细胞肺癌（non-small cell lung cancer, NSCLC）在影像学上可见肿大淋巴结，但其良恶性难以直接判断，为肿瘤分期及设计放疗靶区带来了困难。临床上常依据淋巴结短径≥1 cm或最大标准摄取值≥2.5诊断淋巴结为恶性，但这些诊断标准敏感性和特异性均较低，难以满足临床需要。近年来，基于其他影像学参数诊断淋巴结良恶性取得了众多进展，同时随着影像组学、深度学习等技术的发展，通过挖掘肿大淋巴结区域的影像信息建立模型进而提升淋巴结良恶性诊断准确性展现出巨大的应用前景。本文旨在综述近年来基于影像学诊断NSCLC肿大淋巴结良恶性的研究进展，以便临床工作中更准确且无创地评估淋巴结状态。 】 【中文关键词：肺肿瘤；影像学；淋巴结；影像组学；深度学习】.

Keywords: Deep learning; Lung neoplasms; Lymph node; Radiography; Radiomics.

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