A clustering-based approach to address correlated features in predicting genitourinary toxicity from MRI-guided prostate SBRT.

Background: It is common in outcome analysis to work with a large set of candidate prognostic features. However, such high-dimensional input and relatively small sample size leads to risk of overfitting, low generalizability, and correlation bias.

Purpose: This study addresses the issue of correlation bias mitigation in the context of predicting genitourinary (GU) toxicity in prostate cancer patients underwent MRI-guided stereotactic body radiation therapy (SBRT).