Sun Mengmeng, Deng Xinyi, Xing Hui, Zhang Rui, Fu Bingbing, Ai Tao, Wang Fang, Wang Xuechun, Chen Lei, Mao Xiaogang, Wu Feng
Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei, University of Arts and Science, Xiangyang, People's Republic of China.
School of Medicine, Wuhan University of Science and Technology, Wuhan, People's Republic of China.
Int J Womens Health. 2025 May 9;17:1321-1332. doi: 10.2147/IJWH.S512216. eCollection 2025.
The aim of this study is to develop a predictive model for the therapeutic efficacy of high-intensity focused ultrasound (HIFU) ablation in the treatment of adenomyosis, utilizing dual-sequence MRI radiomics.
A retrospective analysis was conducted on 114 patients diagnosed with adenomyosis who underwent ultrasound-guided HIFU ablation under conscious sedation between July 2021 and July 2023. Patients were randomly allocated into a training set and a test set at a ratio of 7:3. The study aimed to evaluate the distribution of clinical characteristics among patients experiencing effective versus ineffective ablation at two distinct classification thresholds (0.7 and 0.5). Multiple models were developed to explore the combination of effective radiomic features derived from dual-sequence MRI and clinical data. Radiomic features were extracted from the MRI images of adenomyosis lesions in the training set. This process included feature extraction, selection, model construction, and evaluation. Logistic regression was used to construct the predictive model, and its performance was assessed on the test set using the receiver operating characteristic (ROC) curve. The Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the predictive accuracy of the models.
The predictive model showed better alignment with actual ablation outcomes, particularly for predicting ablation success rates exceeding 50%. The combination of radiomic features from the two MRI sequences achieved an AUC of 0.84 in the test set. Decision curve analysis demonstrated that the combined model provided greater net benefit than the single-sequence radiomics model across a broader range of risk thresholds. For the prediction of 70% efficacy, the combined model achieved an AUC of 0.804 in the test set, slightly lower the 50% efficacy prediction task.
The model, based on dual-sequence MRI radiomics, emerges as a promising tool for predicting the efficacy of HIFU ablation, potentially aiding clinicians in anticipating the outcomes of HIFU ablation procedures.
本研究旨在利用双序列MRI放射组学开发一种预测高强度聚焦超声(HIFU)消融治疗子宫腺肌病疗效的模型。
对2021年7月至2023年7月期间114例诊断为子宫腺肌病且在清醒镇静下接受超声引导下HIFU消融的患者进行回顾性分析。患者按7:3的比例随机分为训练集和测试集。本研究旨在评估在两个不同分类阈值(0.7和0.5)下,消融有效与无效患者的临床特征分布情况。开发了多个模型来探索从双序列MRI和临床数据中得出的有效放射组学特征的组合。从训练集中子宫腺肌病病变的MRI图像中提取放射组学特征。这个过程包括特征提取、选择、模型构建和评估。使用逻辑回归构建预测模型,并使用受试者工作特征(ROC)曲线在测试集上评估其性能。使用德龙检验、净重新分类改善(NRI)和综合判别改善(IDI)来比较模型的预测准确性。
预测模型与实际消融结果显示出更好的一致性,特别是在预测消融成功率超过50%时。来自两个MRI序列的放射组学特征组合在测试集中的AUC为0.84。决策曲线分析表明,在更广泛的风险阈值范围内,组合模型比单序列放射组学模型提供了更大的净效益。对于70%疗效的预测,组合模型在测试集中的AUC为0.804,略低于50%疗效预测任务。
基于双序列MRI放射组学的模型成为预测HIFU消融疗效的有前景的工具,可能有助于临床医生预测HIFU消融手术的结果。