Machine learning approaches for survival prediction and risk-stratified treatment guidance in synchronous metastatic nasopharyngeal carcinoma: A multicenter study.

ABSTRACT:

Objective

Synchronous metastatic nasopharyngeal carcinoma (smNPC) demonstrates marked prognostic heterogeneity, which cannot be captured by conventional Cox proportional hazards (CoxPH) models owing to their limitations in managing complex, nonlinear relationships. We employed various machine learning (ML) techniques to enhance predicting overall survival (OS) in patients with smNPC.

Methods

In this multicenter retrospective study, we conducted an analysis of 404 patients diagnosed with smNPC. We developed and assessed prognostic models utilizing a traditional Cox proportional hazards model alongside six ML algorithms. The predictive performances of these models were compared using the concordance index (C-index) and time-dependent area under the curve (tAUC). Risk stratific

SUBMITTER: Long D 

PROVIDER: S-EPMC12682990 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature