Diagnostic value of intraoperative ultrasonography in assessing thoracic recurrent laryngeal nerve lymph nodes in patients with esophageal cancer

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Abstract
The incidence of recurrent laryngeal nerve (RLN) injury has increased due to RLN lymph node dissection. The aim of this study was to evaluate the ability of intraoperative ultrasonography (IU) to detect RLN nodal metastases in esophageal cancer patients. Sixty patients with esophageal cancer underwent IU, computed tomography (CT), and endoscopic ultrasonography (EUS) to assess for RLN nodal metastasis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared. The sensitivities of IU, CT, and EUS in diagnosing right RLN nodal metastases were 71.4, 14.3, and 30.0%, respectively, and a significant difference among these three examinations was observed (χ2 = 10.077, P = .006). The specificities of IU, CT, and EUS for diagnosing right RLN nodal metastasis were 67.4, 97.8, and 95.0%, respectively, and a significant difference was observed (χ2 = 21.725, P < .001). No significant differences in either PPV or NPV were observed when diagnosing right RLN nodal metastases. For diagnosis of left RLN lymph nodal metastases, the sensitivities of IU, CT, and EUS were 91.7, 16.7, and 40.0% respectively. There was a significant difference among these diagnostic sensitivities (χ2 = 14.067, P = .001). The specificities of IU, CT, and EUS for diagnosis of left RLN nodal metastases were 79.2, 100, and 82.5%, respectively and a significant difference was observed (χ2 = 10.819, P = .004). No significant differences were observed in PPV or NPV for these examinations when diagnosing left RLN nodal metastases. Intraoperative ultrasonography showed superior sensitivity compared with preoperative CT or EUS in detecting RLN lymph node metastasis in patients with thoracic esophageal cancer.
Funding Information
  • National Natural Science Foundation of China (81402003)
  • the Fundamental Research Funds for the Central Universities (13ykpy49)
  • Guangdong Science and Technology Program (2013B021800170)

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