Review and prospects of new progress in intelligent imaging research on lymph node metastasis in esophageal carcinoma

Abstract

Esophagus carcinoma (EC) ranks sixth in cancer-related mortality and seventh in terms of morbidity worldwide, and radical esophagectomy is considered as the basis of comprehensive treatment for locally advanced EC. Accurate preoperative determination of lymph node status is critical for treatment decision-making, assessment of survival time and life quality of patients after surgery. However, the rate of misdiagnosis and missed diagnosis of metastatic lymph nodes by traditional imaging methods is high. With the development of artificial intelligence technology and medical image digitization, medical image analysis methods based on artificial intelligence have brought new ideas to the diagnosis and research of lymph node metastasis secondary to EC. At present, texture analysis, radiomics and deep learning are the most widely used methods. These technologies extract and analyze quantitative features from traditional medical images to provide biological information such as tumor characteristics and heterogeneity to guide clinical practice. Therefore, this review mainly introduces and discusses the current status of imaging research on lymph node metastasis in patients with EC based on texture analysis, radiomics and deep learning, and prospects the important research directions in the future with a view to improving the diagnostic capability of lymph node metastasis in patients with EC in China.