Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data.

ABSTRACT:

Background

Despite significant improvements in treatment efficacy, nasopharyngeal carcinoma (NPC) remains one of the most common and threatening head and neck cancers. This study sought to build a risk stratification model that categorizes NPC patients into distinct prognostic groups based on readily available demographic and clinical variables, with the goal of enhancing patient care and improving survival rates.

Methods

Data for patients diagnosed with NPC between 2010 and 2018 were obtained from the SEER program. Kaplan-Meier analysis and Cox proportional hazards regression were applied to evaluate the effects of treatment modalities on overall survival (OS) and cancer-specific survival (CSS). Prognostic variables from multivariable Cox models were used to construct nomog

SUBMITTER: Lin G 

PROVIDER: S-EPMC12605841 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature