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.

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