A nomogram for predicting overall survival of patients with sinonasal melanoma: A population‐based study. Issue 6 (22nd November 2022) - Yang, Jingyi; Song, Xiaole; Lai, Yuting; Liu, Quan; Sun, Xicai; Wang, Dehui; Yu, Hongmeng
Abstract: Objective: Sinonasal melanoma (SMM) is a rare but aggressive malignancy with 5‐year overall survival (OS) rates below 40% in published studies. However, the clinicopathological predictors of the prognosis of SMM remain undefined. We aimed to establish a model to predict the survival outcomes of SMM. Methods: We searched the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with SMM between 1975 and 2016. Data on patient demographics, treatment modalities, and survival outcomes were retrieved. Risk factors for OS were evaluated by survival and Cox regression analyses. We also developed and validated a nomogram for OS, and compared its performance with that of conventional staging systems. Results: Overall, 305 SMM patients were included in this population‐based study. Multivariate Cox regression showed that primary site, American Joint Committee on Cancer stage, radiotherapy, and surgery were significant risk factors for survival. A nomogram was established using the regression model. The C‐indices, areas under the receiver operating characteristic curves, calibration plots, and decision curve analysis demonstrated reliable performance of the nomogram. Conclusion: The nomogram predicting survival outcomes of SMM patients based on clinical information showed good discriminative ability and prognostic accuracy compared with conventional stage classifications. Our nomogram could be used to predict the survival probabilities for SMM patients at different timepoints. Level of Evidence: 2b. Abstract : The Surveillance, Epidemiology, and End Results (SEER) database was searched for patients diagnosed with sinonasal melanoma (SMM) between 1975 and 2016. Multivariate Cox regression showed that primary site, AJCC staging, radiotherapy, and surgery were significant risk factors for survival. A nomogram predicting survival outcomes of SMM patients was established and could be used to predict the survival probabilities for SMM patients at different timepoints.