Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Head and Neck Mucosal Melanoma

用于预测头颈部黏膜黑色素瘤患者总生存期和癌症特异性生存期的预后列线图

阅读:2

Abstract

BACKGROUND: Accurate forecasting of the risk of death is crucial for people living with head and neck mucosal melanoma (HNMM). We aimed to establish and validate an effective prognostic nomogram for HNMM. METHODS: Patients with HNMM who underwent surgery between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database for model construction. After eliminating invalid and missing clinical information, 288 patients were ultimately identified and randomly divided into a training cohort (199 cases) and a validation cohort (54 cases). Univariate and multivariate Cox proportional hazards regression analyses were performed in the training cohort to identify prognostic variables. Independent influencing factors were used to build the model. Through internal verification (training cohort) and external verification (validation cohort), the concordance indexes (C-indexes) and calibration curves were used to evaluate the predictive value of the nomogram. RESULTS: For the training cohort, five independent risk predictors, namely age, location, T stage, N stage, and surgery, were selected, and nomograms with estimated 1- and 3-year overall survival (OS) and cancer-specific survival (CSS) were established. The C-index showed that the predictive performance of the nomogram was better than that of the TNM staging system and was internally verified (through the training queue: OS: 0.764 vs 0.683, CSS: 0.783 vs 0.705) and externally verified (through the verification queue: OS: 0.808 vs 0.644, CSS: 0.823 vs 0.648). The calibration curves also showed good agreement between the prediction based on the nomogram and the observed survival rate. CONCLUSION: The nomogram prediction model can more accurately predict the prognosis of HNMM patients than the traditional TNM staging system and may be beneficial for guiding clinical treatment.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。