Prognostic risk factors and nomogram construction for sebaceous carcinoma: A population-based analysis

Abstract

BackgroundSebaceous gland carcinoma (SGC) is a rare tumor for which there are currently no effective tools to predict patient outcomes. We analyzed the clinical and pathological prognostic risk factors of sebaceous carcinoma based on population data and created a nomogram of related risk factors, which can more accurately predict the 3-, 5-, and 10-year overall survival (OS) rates of patients.MethodsSGC patients between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to training and validation cohorts. Relevant risk factors were identified by univariate and multivariate COX hazards regression methods and combined to produce a correlation nomogram. The concordance index (C-index), the area under the receiver operating characteristic (AUC) curve, and calibration plots have demonstrated the predictive power of the nomogram. Decision curve analysis (DCA) was used to measure nomograms in clinical practice.ResultsA total of 2844 eligible patients were randomly assigned to 70% of the training group (n=1990) and 30% of the validation group (n=854) in this study. The derived meaningful prognostic factors were applied to the establishment of the nomogram. 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. The AUC and calibration plots of 3-, 5-, and 10-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.ConclusionWe created a novel nomogram of prognostic factors for SGC, which more accurately and comprehensively predicted 3-, 5-, and 10-year OS in SGC patients. 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 SGC patients.