Prediction of lateral neck metastasis in patients with papillary thyroid cancer with suspicious lateral lymph ultrasonic imaging based on central lymph node metastasis features

Neck lymphatic metastasis is a common occurrence with thyroid cancers, and pre operative lateral lymph node metastasis (LLNM) and postoperative lateral lymph node recurrence (LLNR) are two independent risk factors that are negatively associated with the prognosis of patients with thyroid cancer. The aim of the present study was to investigate the relationship between central lymph node metastasis (CLNM) and LLNM in patients with papillary thyroid carcinoma (PTC) with sonographically suspected LLNM, such as those without lymph node fine-needle aspiration (FNA) cytological results or negative FNA results at the time of diagnosis. The predictive ability of CLNM regarding LLNR was also investigated. The present study retrospectively reviewed the clinical data of 1,061 patients that were surgically treated for PTC and 128 patients with sonographically suspected lateral lymph nodes that received central lymph node dissection and lateral lymph node dissection at the Thyroid Department of The First Affiliated Hospital of Anhui Medical University (Hefei, China) from June 2019 to June 2021. In patients with suspicious ultrasonic images suggesting LLNM, a significant association between the central lymph node ratio (CLNR), the number of positive central lymph nodes and LLNM was demonstrated. Otherwise, there were no statistically significant differences between the CLNR in patients with PTC and patients with PTC without evidence of lateral cervical metastasis. However, the rate of LLNR increased significantly when the number of positive central lymph nodes was >3. In conclusion, the CLNR and the number of positive central lymph nodes could be used to predict LLNM in patients with PTC with sonographically suspect lateral lymph nodes, including those with no FNA cytological results or negative FNA results, which may potentially support physicians in making personalized clinical decisions.