Prediction of depth of invasion and lymph node metastasis in superficial pharyngeal cancer by magnifying endoscopy using the Japan Esophageal Society classification

DEN open(2023)

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摘要
Backgrounds: The pharynx has no muscularis mucosae, so it is unclear whether diagnostic techniques used for the esophagus can be applied to the pharynx. This study investigated the usefulness of magnifying endoscopy with narrowband imaging using the Japan Esophageal Society (JES) classification for predicting the depth of invasion and lymph node metastasis (LNM) in pharyngeal cancer. Methods: A total of 123 superficial pharyngeal carcinoma lesions that had been observed preoperatively with magnifying endoscopy with narrowband imaging between January 2014 and June 2021 were analyzed. Predictors of subepithelial invasion (SEP) and LNM were sought based on endoscopic findings, including microvascular morphology, using the JES classification. Results: The lesions were divided into carcinoma in situ (n = 41) and SEP (n = 82). Multivariate analysis identified B2-B3 vessels (odds ratio [OR] 6.54,95% confidence interval [CI] 1.74-24.61, p = 0.005) and a middle/large avascular area (OR 4.15, 95% CI 1.18-14.62, p = 0.027) as independent predictors of SEP. Significant predictors of LNM were protruding type, B2-B3 vessels, middle/large avascular area, SEP, venous invasion, lymphatic invasion, and tumor thickness > 1000 mu m. Median tumor thickness increased significantly in the order of B1 < B2 < B3 vessels (B1, 305 mu m; B2, 1045 mu m; B3, 4043 mu m; p < 0.001). The LNM rates for B1, B2, and B3 vessels were 1.6% (1/63), 4.8% (2/42), and 55.6% (10/18), respectively (p < 0.001). Conclusions: Magnifying endoscopy with narrowband imaging using the JES classification could predict the depth of invasion in superficial pharyngeal carcinoma. The JES classification may contribute to the prediction of LNM, suggesting that it could serve as an alternative to tumor thickness.
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关键词
JES classification,magnifying endoscopy,narrowband imaging,pharyngeal cancer,tumor thickness
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