Keywords: Diagnosis/Prediction, Radiomics, MR-HIFU, Treatment of Uterine Fibroids, XGBoost, SHAP, LR, Coefficients
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.
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