Development of a Nomogram Based on Clinicopathological and Biological Features to Predict Neck Lymph Node Metastasis in Hypopharyngeal Squamous Cell Carcinoma

Background: Clinicopathological and biological features are associated with neck lymph node metastasis (LNM) of hypopharyngeal squamous cell carcinoma (HSCC). However, there is no complete nomogram combining multiple factors that can be used to accurately predict the neck LNM status for HSCC patients. Purpose: To guide the selection of surgical methods and radiotherapy areas for hypopharyngeal cancer. In this study, a nomogram was developed to combine these risk factors to predict neck LNM and guide the treatment of HSCC. Material and Methods: This retrospective study included 117 patients (training cohort, 64 patients;trial cohort, 53 patients). Biological characteristics of HSCC patients were assessed using immunohistochemical staining, and data of patient age, gender, and preoperative computed tomography (CT) scan reports were collected. Significant risk factors in univariate analysis were further identified to be independent variables in multivariate logistic regression analysis, which were then incorporated in and presented with a nomogram by using the rms package in R software. Receiver operating characteristic (ROC) curves and calibration curves were used to validate the discrimination and accuracy in the training and validation cohorts, respectively, and clinical usefulness was verified in decision curve analysis curves. Results: All variables with P-values < 0.2 in the univariate analysis were selected for multivariate logistic regression analysis to further identify independent risk factors for neck LNM. In multivariate logistic regression analysis, variables with P-values < 0.2 were identified as independent risk factors and then used to construct the nomogram. In total, five independent predictors, including the maximum tumor diameter in CT, tumor cell differentiation, LNM status in CT, Stathmin1 expression level, and lymphatic vessel invasion were included in the nomogram. The area under the ROC curve (AUC) was 0.916 (95% confidence interval [CI], 0.833 - 1.000) and AUC of 0.928 (95% CI, 0.864 - 1.000) in internal validation and the external validation. Conclusions: Both the internal validation in the training cohort and the external validation in the validation cohort showed that the nomogram had good discrimination, accuracy, and excellent clinical usefulness. The nomogram based on clinicopathological and biological features developed in this study has strong predictive power and could be used to predict neck LNM of HSCC in clinical practice.