Utilizing XGBoost and LR to find the significant predictive factors of MR-guided high intensity focused ultrasound ablation in uterine fibroids

Abstract

Motivation: MR-HIFU offers a new treatment option for women with uterine fibroids. However, there is currently a lack of quantitative models to predict the efficacy of MR-HIFU based on T2WI of fibroids for guiding preoperative clinical decisions. Goal(s): We hope to identify the most important predictive factors of MR-HIFU treatment for uterine fibroids and predict the efficacy using radiomics data combine with clinical data. Approach: We employed XGBoost and logistic regression (LR) to build two prediction models. SHAP values of XGBoost and LR coefficients were used to pinpoint significant predictive factors. Results: Both models achieved outstanding results and the significant predictive factors are consistent. Impact: Our excellent model results have identified the optimal predictive factors for assessing the efficacy of MR-HIFU in the treatment of uterine fibroids. These factors aid physicians in preoperative guidance and clinical strategy formulation, clarifying which patients will achieve better outcomes.