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Abstract：
This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consists of four base classifiers and was evaluated using accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve (AUC). The predictive performance of the combined model combining radiomics and deep learning features outperformed the radiomics and deep learning feature models alone, with accuracy of 0.848 and 0.814 in training and test sets, and AUC of 0.916 and 0.861, respectively. Compared with the base classifiers that make up the multi-model fusion model, the fusion model also exhibited better prediction performance. The fusion model incorporating both radiomics and deep learning features had certain predictive value for the ablation rate of adenomyosis under HIFU therapy and could help select patients with adenomyosis who would benefit from HIFU therapy.
摘要：
本研究旨在开发一种基于影像组学和深度学习特征的模型，以预测接受高强度聚焦超声（HIFU）治疗的子宫腺肌病患者的消融率。回顾性分析119例接受HIFU治疗的子宫腺肌病患者。参与者以7：3的比例被纳入培训和测试队列。从T2加权成像（T2WI）图像中提取影像组学特征，VGG-19用于提取高级深层特征。提出了一种基于多模型融合的集成模型，用于预测子宫腺肌病HIFU的疗效。它由四个基分类器组成，并使用精度进行了评估，精度,召回，F分数,和接受者工作特征曲线下面积(AUC)。结合影像组学和深度学习特征的组合模型的预测性能优于单独的影像组学和深度学习特征模型，训练集和测试集的准确度为0.848和0.814，AUC分别为0.916和0.861。与构成多模型融合模型的基分类器相比,融合模型也表现出更好的预测性能。融合影像组学和深度学习特征的融合模型对HIFU治疗下子宫腺肌病的消融率具有一定的预测价值，可以帮助选择从HIFU治疗中受益的子宫腺肌病患者。
