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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