BACKGROUND: Sinonasal squamous cell carcinoma (SNSCC) is a rare malignancy with varied outcomes. The aim of this study was to develop a nomogram for predicting survival of patients with SNSCC. METHODS: From the Surveillance, Epidemiology, and End Results database, we identified 1766 patients diagnosed with SNSCC between 2004 and 2015. Patients were randomly separated into a training set and a validation set in 4:1 ratio. An external validation was also performed by a set of 74 SNSCC patients who had been treated in our department. We used the training set to build a nomogram based on stratified multivariable Cox proportional hazard models for predicting overall survival. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index and calibration curve. RESULTS: Based on 1412 cases of the training cohort, our Cox regression analysis revealed that age, marital status, primary site, differentiation, T stage, N classification, M stage, and treatment modalities were associated with overall survival. A nomogram was established based on the results of multivariate analysis. The C-index values of the nomogram for predicting survival were superior to those of the tumor-node-metastasis staging system (0.745 vs 0.679 in the training cohort, 0.752 vs 0.656 in the validation set, and 0.678 vs 0.596 in the external validation set). The calibration plots demonstrated good consistency between the predicted and observed results. CONCLUSION: We have developed a nomogram to accurately predict the clinical outcomes of SNSCC patients. This model was effective and can help clinicians to improve patient counseling.
BACKGROUND:Sinonasal squamous cell carcinoma (SNSCC) is a rare malignancy with varied outcomes. The aim of this study was to develop a nomogram for predicting survival of patients with SNSCC. METHODS: From the Surveillance, Epidemiology, and End Results database, we identified 1766 patients diagnosed with SNSCC between 2004 and 2015. Patients were randomly separated into a training set and a validation set in 4:1 ratio. An external validation was also performed by a set of 74 SNSCC patients who had been treated in our department. We used the training set to build a nomogram based on stratified multivariable Cox proportional hazard models for predicting overall survival. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index and calibration curve. RESULTS: Based on 1412 cases of the training cohort, our Cox regression analysis revealed that age, marital status, primary site, differentiation, T stage, N classification, M stage, and treatment modalities were associated with overall survival. A nomogram was established based on the results of multivariate analysis. The C-index values of the nomogram for predicting survival were superior to those of the tumor-node-metastasis staging system (0.745 vs 0.679 in the training cohort, 0.752 vs 0.656 in the validation set, and 0.678 vs 0.596 in the external validation set). The calibration plots demonstrated good consistency between the predicted and observed results. CONCLUSION: We have developed a nomogram to accurately predict the clinical outcomes of SNSCC patients. This model was effective and can help clinicians to improve patient counseling.