A nomogram for prediction of distant metastasis in patients with hypopharyngeal squamous cell carcinoma: a study based on the SEER database.

ABSTRACT

BACKGROUND: The prognosis of hypopharyngeal squamous cell carcinoma (HPSCC) is poor due to its high incidence of local invasion and distant metastasis (DM). This study aims to explore the DM risk factors of HPSCC and establish a clinical prediction model. METHODS: We downloaded patient data from the Public Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2018. Univariate and multivariate logistic regression analyses were performed to screen the clinical risk factors for DM of HPSCC. A new nomogram prediction model was then established based on the selected clinical risk factors. We further validated the model's accuracy based on the concordance index (C-index), the area under the receiver operating characteristic (AUC) curve, and the calibration plot. The decision curve analysis (DCA) to test the potential clinical value of the new model was also applied. RESULTS: A total of 3502 patients were enrolled; the patients with HPSCC were randomly assigned to a training set (n=2463) and a validation set (n=1039). Multivariate Logistic model analysis suggested that sex, T stage, N stage, and the total number of tumors were influence factors for DM of HPSCC. We established and validated a novel nomogram prediction model based on the multivariate logistic model with these influence factors. The C-index was 0.943 and 0.849 in the training and validation sets respectively. The AUC of the training set was 0.705 (95% CI 0.669-0.741), and the validation set was 0.667 (95% CI 0.609-0.725). The calibration plot shows that the actual observation value was similar to the predicted value, meaning the model has an excellent discrimination ability. DCA of the nomogram in the training and validation sets suggested that our nomogram has potential application value. CONCLUSIONS: We found that sex, T stage, N stage, and the total number of tumors are independent risk factors for DM of HPSCC. We developed a novel prediction model to predict DM in patients with HPSCC. This nomogram can identify patients with a high risk of DM and has a high clinical application value.