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.

ABSTRACT: 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

SUBMITTER: Fang H 

PROVIDER: S-EPMC12531292 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature