Abstract

BackgroundThere are no effective preoperative diagnostic measures to predict the probability of left and right recurrent laryngeal nerve (RLN) lymph node (LN) metastasis using preoperative clinical data in patients undergoing thoracolaparoscopic esophagectomy with cervical anastomosis.MethodsWe retrospectively reviewed the clinical data of 1,660 consecutive patients with thoracic esophageal cancer who underwent esophagectomy with cervical anastomosis at the Department of Thoracic Surgery at the First Affiliated Hospital of Zhengzhou University between January 2015 and December 2020.ResultsA total of 299 and 343 patients who underwent left (Cohort 1) and right (Cohort 2) RLN LN dissection were included in the final analyses. The analyses were conducted within each cohort. Among the 299 patients in Cohort 1, left RLN LN involvement was found in 41 patients (13.7%). A multivariable analysis showed that age, tumor location, and short axis were significantly associated with RLN LN metastasis (all P<0.05). Among the 343 patients in Cohort 2, right RLN LN involvement was found in 65 patients (19.0%). A multivariable analysis showed that computed tomography (CT) appearance, tumor location, long axis, and short axis were significantly associated with RLN LN metastasis (all P<0.05). Based on the results of the multivariable analyses, we constructed nomograms that could estimate the probability of RLN LN metastasis. Finally, we stratified the 2 cohorts into risk subgroups using a recursive partitioning analysis (RPA). The risk of left and right RLN LN metastasis was found to be 9.3% and 7.5%, 27.3% and 21.4%, and 52.4% and 47.3% for the low-risk, intermediate-risk, and high-risk groups, respectively.ConclusionsOur nomograms and RPAs appear to be suitable for the risk stratification of left and right RLN LN metastasis in patients undergoing thoracolaparoscopic esophagectomy with cervical anastomosis. This tool could be used to help clinicians to select more effective locoregional treatments, such as surgical protocols and radiation area selection.

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