Prediction of pediatric Wilms tumor recurrence using interpretable machine learning models: insights from a 20-year real-world study and the prognostic value of Ki-67.

Despite overall survival exceeding 80% in Wilms tumor (WT), approximately 15% of pediatric patients experience recurrence with poor post-relapse survival (~50%) and significant long-term complications, highlighting an unmet need for precise recurrence prediction. To address this, we developed and validated machine learning (ML) models for predicting postoperative WT recurrence using real-world clinical data. Among 476 pediatric WT patients who underwent radical surgery at our institution (June 2004-June 2024), 351 met inclusion criteria and were randomized into training (70%) and validation (30%) cohorts. Seven independent predictors - COG tumor stage, age, tumor rupture, histological subtype (COG classification), tumor thrombus, tumor volume, and Ki-67 index - were identified via feature selection intersection of Boruta, LASSO, subgroup analysis, and univariate logistic regression. Predictive models were constructed using nine ML algorithms (DT, LASSO, KNN, LightGBM, LR, MLP, RF, SVM, XGBoost), with performance evaluated using metrics such as AUC, accuracy, F1 score, specificity, positive predictive value (PPV), negative predictive value (NPV), and confusion matrix. Among the included patients, 51 (14.53%) experienced tumor recurrence (including 7 multi-site relapses), with median time to recurrence 6 months (IQR 4-16 months) and 80.4% occurring within the first postoperative year. In the validation cohort, the SVM model demonstrated the best performance (AUC = 0.851; accuracy = 0.830; specificity = 0.856; F1 = 0.550; PPV = 0.458; NPV = 0.939), and SHAP analysis highlighted unfavorable histology, COG stage IV-V, tumor thrombus, and elevated Ki-67 index as the strongest contributors to recurrence risk. This interpretable SVM-based model confirms seven key predictors - especially the Ki-67 index - and serves as a practical risk stratification tool to support individualized follow-up planning.