Comparison of random survival forest and Cox regression for long-term prognosis of pediatric Wilms tumor: based on the SEER database and external validation cohort from China

We successfully developed a robust machine learning model that accurately assesses key prognostic factors affecting the long-term survival of children with WT. This model offers substantial clinical value for risk stratification and can assist clinicians in making more informed treatment decisions. By applying SHAP analysis, physicians can better understand the critical factors influencing WT prognosis and tailor intervention strategies more precisely.