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

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 stratification was conducted to guide personalized treatment strategies, and Kaplan–Meier survival curve analyses were performed to evaluate the efficacy of different treatment regimens across subgroups. Results The random survival forest (RSF) model yielded the highest C-index of 0.746 and an average tAUC of 0.801 for OS, identifying RSF as the optimal predictor. Shapley additive explanations analysis revealed that five features were most influential: number of metastatic lesions, involved organs, first-line regimen, presence of liver metastasis, and using immunotherapy. Risk stratification, based on Kaplan–Meier survival curve analyses, demonstrated that local treatment of metastatic lesions significantly extended OS in low- and high-risk patient cohorts. Primary site radiotherapy conferred a survival advantage exclusively to low-risk patients, whereas immunotherapy yielded improved outcomes in high-risk patients (all p < 0.05). Conclusions The RSF model excelled at predicting OS for patients with smNPC, providing reliable prognostic insights to guide risk-based treatment decisions in clinical practice.