Nomogram prediction for the overall survival and cancer-specific survival of patients diagnosed with Merkel cell carcinoma

Abstract

BACKGROUND: Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine carcinoma of the skin, with a high recurrence rate and a high mortality rate worldwide. The purpose of this article is to construct a nomogram that incorporates significant clinical parameters and predicts the survival of individuals with MCC. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was employed to retrospectively analyze all confirmed MCC cases from 2004 to 2015. The data was collected from 3,688 patients, and was randomized as the training or validation group (1:1 ratio). The independent factors which predicted the cancer-specific survival (CSS) and overall survival (OS) for MCC cases were searched for nomogram construction respectively. Independent parameters that affected CSS were determined using the Fine and Gray competing risk regression model. In addition, the time-dependent receiver operating characteristic (ROC) curve was constructed. Then, the area under the curve (AUC) values, calibration curve, and the concordance index (C-index) were used to determine the nomogram performance. At last, decision curve analysis (DCA) was conducted to determine the net clinical benefit. RESULTS: The multivariate analysis results revealed that sex, age, race, marriage, American Joint Committee on Cancer (AJCC) stage, chemotherapy and radiotherapy were independent OS prognostic factors. Furthermore, competing risk analysis showed age, sex, AJCC stage, chemotherapy were the independent CSS prognostic factors. For validation, the C-index value of OS nomogram was 0.703 (95% CI: 0.686-0.721), while C-index value of CSS nomogram was 0.737 (95% CI: 0.710-0.764). Both C-index and AUC suggested that nomograms had superior performance to that of the AJCC stage system. In addition, according to the calibration curve, both nomograms were capable of accurate prediction of MCC prognosis. The DCA showed that the net benefits of the nomograms were superior among various threshold probabilities than these of AJCC stage system. CONCLUSIONS: The present work established and verified the novel nomograms to predict the OS and CSS of MCC patients. If further confirmed in future studies, it may become another helpful tool for risk stratification and management of MCC patients.