A nomogram for predicting occult lymph node metastasis in early hypopharyngeal cancer with cN0

Purpose To explore the risk factors of cervical occult lymph node metastasis (OLNM) in early cN0 hypopharyngeal squamous cell carcinoma (HPSCC), and construct a nomogram model to predict the risk of OLNM in patients with early cN0 HPSCC. Methods 78 cases of early (T-1-T-2) HPSCC patients who underwent hypopharyngectomy were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to determine independent risk factors and a nomogram was constructed according to the results of the multivariate logistic regression analysis. Model performance was assessed by constructing a receiver operating characteristic (ROC) curve, and discriminatory capacity assessed using the area under the curve (AUC). Calibration was completed using a plotted calibration curve accompanied by the Hosmer-Lemeshow test. Results Multivariate logistic regression analysis revealed that age (OR 0.928, 95% CI 0.863-0.997), history of drinking (OR 6.668, 95% CI 1.724-25.788), histological differentiation of tumor (OR 7.269, 95% CI 1.000-52.820), depth of invasion (OR 5.046, 95% CI 1.281-19.874) were independent risk factors of OLNM in early cN0 HPSCC. The ROC curve had an AUC of 0.811 (95% CI 0.713-0.909), which implies good discriminate capacity. The calibration curve and the Hosmer-Lemeshow test (P = 0.972) demonstrated good model fitted and high calibration. Conclusion A nomogram model based on age, drinking history, histological differentiation of tumor, and depth of tumor invasion was successfully developed to predict occult cervical lymph node metastasis in patients with early cN0 hypopharyngeal cancer.

Automated summary

This study identified age, history of drinking, histological differentiation of tumor, and depth of tumor invasion as independent risk factors for occult lymph node metastasis in early cN0 hypopharyngeal squamous cell carcinoma. A nomogram model was successfully developed to predict the risk of OLNM in these patients. The model showed good discrimination and calibration performance.