Chen Yuan, Liu Mali, Huang Deqing, Liu Ziyi, Yang Aisen, Qin Na, Shu Jian
Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China (Y.C., D.H., Z.L., A.Y., N.Q.).
Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, China (M.L., J.S.).
Acad Radiol. 2025 Mar;32(3):1488-1499. doi: 10.1016/j.acra.2024.09.040. Epub 2024 Oct 30.
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.
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.
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.
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.
本研究旨在应对预测接受高强度聚焦超声（HIFU）消融治疗的子宫肌瘤患者治疗效果这一挑战。我们开发了医学辅助诊断模型，以准确预测消融率和体积缩小率，从而评估子宫肌瘤的短期和长期治疗效果。
对于消融率预测，我们的研究纳入了348个肌瘤，分为181个完全消融肌瘤和167个消融不完全肌瘤。利用多模态MRI序列和临床特征，结合特征提取、测试和筛选等数据预处理步骤，我们构建了一个预测术前消融率的集成模型。在体积缩小率研究中，我们分析了253个肌瘤，分为142个高体积反应者和111个低体积反应者。基于临床特征和T2加权图像（T2WI）序列，以及病变勾勒、特征归一化等预处理步骤，我们开发了一个预测术前体积缩小率的层间信息融合模型。
集成模型在测试集上的准确率为0.800，曲线下面积（AUC）为0.830，而层间信息融合模型的准确率为0.808，AUC为0.891。与现有模型相比，这两个模型均表现出卓越的预测性能。
本研究中开发的集成模型和层间信息融合模型具有强大的预测能力，为临床医生选择HIFU治疗患者提供了有价值的支持。这些模型有望通过定制治疗方案来改善患者的治疗效果。
