A web-based predictive model for overall survival of patients with cutaneous Merkel cell carcinoma: A population-based study - Xu, Wen; Le, Yijun; Zhang, Jianzhong (Frontiers in Endocrinology, 2022)
Background: Merkel cell carcinoma (MCC) is an aggressive neuroendocrine carcinoma with a high mortality rate, so it is necessary to create models to predict overall survival of MCC. We developed an easy-to-use web-based calculator to predict the OS of MCC patients based on the nomogram. Methods: MCC patients between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to training and validation cohorts. Patients between 2016-2017 serve as the external validation cohort. Relevant risk factors were identified by univariate and multivariate COX hazards regression methods and combined to produce nomograms. The concordance index (C-index), area under the receiver operating characteristic (AUC) curve, and calibration plots have demonstrated the predictive power of the nomograms. Decision curve analysis (DCA) was used to measure nomograms in clinical practice. Patients were divided into three groups according to the scores of the nomogram. Results: A total of 3480 patients were randomly assigned to the training group and validation group in this study. Meaningful prognostic factors were applied to the establishment of nomograms. The C-index for OS was 0.725 (95% CI: 0.706-0.741) in the training cohort and 0.710 (95% CI: 0.683-0.737) in the validation cohort. In the external validation cohort, C-index was 0.763 (95% CI: 0.734–0.792). The C-index of training cohort, validation cohort and external validation cohort for CSS were 0.743 (95% CI:0.725-0.761), 0.739(95%CI:0.712-0.766) and 0.774 (95%CI:0.735-0.813), respectively. The AUC and calibration plots of 1-, 3-, and 5-year OS rates showed that the nomogram had good predictive power. DCA demonstrated that the nomogram constructed in this study could provide a clinical net benefit. Our calculator demonstrated excellent predictive capabilities for better risk grouping of MCC patients. Conclusion: We created novel nomograms of prognostic factors for MCC, which more accurately and comprehensively predicted 1-, 3-, and 5-year OS/CSS in MCC patients. We established a calculator which can easily and quickly calculate the risk grouping of MCC patients by inputting clinically relevant characteristics. This can help clinicians identify high-risk patients as early as possible, carry out personalized treatment, follow-up, and monitoring, and improve the survival rate of MCC patients.