Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study

Objectives To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) with deep learning-based automatic segmentation, for preoperative prediction of the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids. Methods This retrospective study collected data from 360 patients with uterine fibroids who underwent HIFU treatment. The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60). Patients were categorized into favorable and unfavorable prognosis groups based on the post-treatment non-perfused volume ratio. Automated segmentation of uterine fibroids was performed using a V-net deep learning models. Radiomics features were extracted from T2WI and CE-T1WI, followed by data preprocessing including normalization and scaling. Feature selection was performed using t -test, Pearson correlation, and LASSO to identify the most predictive features for preoperative prognosis Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) were employed as base learners to construct base predictive models. These models were integrated into a stacking ensemble model, with Logistic Regression serving as the meta-learner to combine the outputs of the base models. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC). Results Among the base models developed using T2WI and CE-T1WI features, the MLP model exhibited superior performance, achieving an AUC of 0.858 (95% CI: 0.756–0.959) in the internal test set and 0.828 (95% CI: 0.726–0.930) in the external test set. It was followed by the SVM, LightGBM, and RF, which obtained AUC values of 0.841 (95% CI: 0.737–0.946), 0.823 (95% CI: 0.711–0.934), and 0.750 (95% CI: 0.619–0.881), respectively. The stacking ensemble learning model, which integrated these five algorithms, demonstrated a notable enhancement in performance, with an AUC of 0.897 (95% CI: 0.818–0.977) in the internal test set and 0.854 (95% CI: 0.759–0.948) in the external test set. Conclusion The DL based automatic segmentation MRI radiomics stacking ensemble learning model demonstrated high accuracy in predicting the prognosis of HIFU ablation of uterine fibroids.