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.
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BACKGROUND: Wilms tumor (WT) is the most common malignant renal tumor in children. Despite advances in treatment, accurate prediction of the long-term prognosis remains challenging. Various Cox regression-based models have been developed to assess WT survival rates; however, there is a pressing need for more precise tools. METHODS: Data from the SEER database (2000-2021) and external validation data from Chongqing Medical University Children's Hospital were utilized. Key prognostic factors for children with WT were identified via last absolute shrinkage and selection operator (LASSO) regression, which was subsequently used to construct the Random Survival Forest(RSF) model for long-term survival prediction. SHAP were applied to enhance the interpretability of the model. The model performance was compared to that of conventional Cox models via calibration curves, the Concordance index (C-index), the net reclassification index (NRI), and the integrated discrimination index (IDI). RESULTS: We included 1,629 children with WT from the SEER database and externally validated the model via data from 169 children at Children's Hospital of Chongqing Medical University(CHCMU). Kaplan‒Meier curves revealed higher mortality rates for Chinese children with WT than for their counterparts in the United States. LASSO regression identified six key variables for the development of the RSF and Cox models. The SHAP method was utilized to rank these variables in descending order of importance: tumor stage, age, lymph node density(LND), tumor metastasis, number of positive lymph nodes, and laterality (unilateral/bilateral). The RSF model demonstrated superior predictive performance and generalizability, as indicated by Brier scores, calibration curves, AUC curves, and risk curves. Moreover, the RSF model significantly outperformed the Cox model in terms of prediction accuracy (C-index: 0.868 vs. 0.759), with substantial improvements in the NRI and IDI (P < 0.01). Decision curve analysis also revealed that the RSF model provided a greater net benefit at 3, 5, and 7 years than did the Cox model, which underscored the greater clinical utility of the RSF model. Sensitivity analysis based on imputed data confirmed the robustness of the model, with consistent predictor selection and comparable performance metrics, further supported the stability and reliability of the RSF framework. CONCLUSION: 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.