Liu Z, Liu Z, Wan X, Wang Y, Huang X
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Clin Radiol. 2025 Feb;81:106778. doi: 10.1016/j.crad.2024.106778. Epub 2024 Dec 15.
To develop a model predicting high-intensity focused ultrasound (HIFU) efficacy in adenomyosis treatment using enhanced T1WI and T2WI-FS radiomics combined with clinical imaging features.
The study included 137 adenomyosis patients treated with HIFU from September 2021 to December 2023. Based on nonperfused volume ratio (NPVR), participants were divided into two groups: NPVR < 50% (n=77) and NPVR ≥ 50% (n=60). Patients were randomly split into training and test sets (7:3 ratio). Radiomics features were extracted from enhanced T1WI and T2WI-FS sequences, while clinical imaging features were selected using univariate analysis and binary logistic regression. Logistic regression models were built for radiomics, clinical imaging, and combined data. Model performance was assessed using ROC curves, Delong's test, and calibration curves.
AUCs for the radiomics, clinical-imaging, and combined models in the training set were 0.831, 0.664, and 0.845, respectively, and 0.829, 0.597, and 0.831 in the test set. The combined model outperformed the clinical-imaging model (training p=0.001, test p=0.01) and the radiomics model (training p=0.012, test p=0.032). However, no significant difference was found between the combined and radiomics models (p>0.05). Calibration curves and decision curve analysis confirmed the combined model's accuracy and clinical applicability.
A model incorporating clinical-imaging features with T1WI and T2WI-FS radiomics effectively predicts HIFU success in adenomyosis treatment, offering valuable guidance for clinical decision-making.
利用增强T1WI和T2WI-FS影像组学结合临床影像特征，建立预测高强度聚焦超声（HIFU）治疗子宫腺肌病疗效的模型。
本研究纳入了2021年9月至2023年12月期间接受HIFU治疗的137例子宫腺肌病患者。根据无灌注体积比（NPVR），将参与者分为两组：NPVR < 50%（n = 77）和NPVR≥50%（n = 60）。患者随机分为训练集和测试集（7:3比例）。从增强T1WI和T2WI-FS序列中提取影像组学特征，同时通过单因素分析和二元逻辑回归选择临床影像特征。构建影像组学、临床影像和联合数据的逻辑回归模型。使用ROC曲线、德龙检验和校准曲线评估模型性能。
训练集中影像组学、临床影像和联合模型的AUC分别为0.831、0.664和0.845，测试集中分别为0.829、0.597和0.831。联合模型优于临床影像模型（训练集p = 0.001，测试集p = 0.01）和影像组学模型（训练集p = 0.012，测试集p = 0.032）。然而，联合模型与影像组学模型之间未发现显著差异（p>0.05）。校准曲线和决策曲线分析证实了联合模型的准确性和临床适用性。
结合临床影像特征与T1WI和T2WI-FS影像组学的模型能有效预测HIFU治疗子宫腺肌病的成功率，为临床决策提供有价值的指导。
