Predicting Short-term and Long-term Efficacy of HIFU Treatment for Uterine Fibroids Based on Clinical Information and MRI: A Retrospective Study.

Abstract

Rationale and objectives: This study aimed to address the challenge of predicting treatment outcomes for patients with uterine fibroids undergoing high-intensity focused ultrasound (HIFU) ablation. We developed medical-assisted diagnostic models to accurately predict the ablation rates and volume reduction rates, thus assessing both short-term and long-term treatment effects of fibroids.

Materials and methods: For the ablation rate prediction, our study included 348 fibroids, categorized into 181 fully ablated and 167 inadequately ablated fibroids. Using multimodal MRI sequences and clinical characteristics, coupled with data preprocessing steps such as feature extraction, testing, and screening, we constructed an ensemble model for predicting preoperative ablation rates. In the volume reduction rate study, we analyzed 253 fibroids, divided into 142 high-volume responders and 111 low-volume responders. Based on clinical characteristics and T2-weighted image (T2WI) sequences, along with lesion delineation, feature normalization, and other preprocessing steps, we developed an inter-slice information fusion model for predicting preoperative volume reduction rates.

Results: The ensemble model demonstrated an accuracy of 0.800 and an area under the curve (AUC) of 0.830 on the test set, while the inter-slice information fusion model achieved an accuracy of 0.808 and an AUC of 0.891. Both models showed superior predictive performance compared to existing models.

Conclusion: The ensemble and inter-slice information fusion models developed in this study exhibit robust predictive capabilities, offering valuable support for clinicians in selecting patients for HIFU treatment. These models hold potential for enhancing patient outcomes through tailored treatment planning.