A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer

A unified machine learning-based model for relapse prediction in clinical stage I testicular cancer is feasible and demonstrates moderate predictive accuracy. It is particularly useful for ruling out relapse and shows greater robustness in non-seminoma. These findings provide a framework for validation in independent cohorts and highlight key predictive features for future research.