Random survival forest predicts survival in patients with metastatic laryngeal and hypopharyngeal cancer and the prognostic benefits of surgery and radiotherapy

Background: Laryngeal and hypopharyngeal cancers are prominent within head and neck malignancies. The diagnosis of distant metastasis (DM) invariably signals poor prognosis, underscoring the need to optimize current treatment approaches. Methods: Patient data for metastatic laryngeal and hypopharyngeal cancer were extracted from the SEER database (2000-2020). Cox regression and propensity score matching (PSM) analyses identified independent prognostic factors and performed stratified survival analyses based on the receipt of primary tumor surgery and radiotherapy. A random survival forest (RSF) model was subsequently developed to predict patient survival. Results: A total of 1,626 patients were included. PSM-based stratified analysis revealed that primary tumor surgery significantly improved survival in patients under 70 years and those with primary laryngeal cancer. Radiotherapy enhanced survival across all age groups, with a benefit primarily for patients with primary laryngeal cancer and squamous-cell carcinoma (SCC). The RSF model demonstrated robust predictive performance, highlighting chemotherapy, primary tumor surgery, and radiotherapy as the top three factors influencing patient survival. Conclusion: The clinical and pathological features of metastatic laryngeal/hypopharyngeal cancer were systematically analyzed using an artificial intelligence (AI) model to predict survival. Subgroup analyses identified patients most likely to benefit from primary tumor surgery and radiotherapy. These findings may guide the development of personalized treatment strategies, potentially improving the prognosis of patients with DM.