超声心动图影像组学数据对肥厚性心肌病心脏重构的预测价值OA
Prediction of cardiac remodeling in patients with hypertrophic cardiomy-opathy using ultrasound radiomics
目的:探讨超声影像组学数据对肥厚性心肌病(HCM)患者心脏重构的预测价值.方法:选择 2021 年 1月1 日至2024 年12 月31 日于成都大学附属医院、华西第四医院就诊的 HCM 患者 265 例,其中确诊心脏重构 92例.采用Logistic 回归模型筛选心脏重构影响因素并构建临床模型.留取心尖四腔心切面图像,提取形状、一阶、纹理和小波等4 类特征,采用 LASSO 回归分析和10 折交叉验证调整参数,筛选最优特征,基于最优特征采用线性回归构建影像组学模型.以最优特征为预测因子,分别构建邻近分类算法模型、多层神经网络算法模型和支持向量机(SVM)模型,并通过 ROC 曲线分析选取性能最优的模型作为最终的深度学习模型.基于上述3 类模型,采用加权融合策略构建融合模型.采用 Bootstrap 1 000 法结合 ROC 曲线评价各模型的性能.结果:临床模型包含 LAD、LVPWd、E/A、LVRI、MVCF、LVMI 等6 个预测因子.影像组学模型包含5 个最优特征,分别为小波_HLH_灰度大小区域矩阵_区域占比、小波_HHH_灰度大小区域矩阵_区域大小不均匀度、小波_LHL_灰度依赖矩阵_依赖方差、梯度_灰度大小区域矩阵_区域方差、小波_LLH_灰度依赖矩阵_依赖不均匀度.筛选 SVM 模型为深度学习模型.上述3 个模型在融合模型中的权重分别为0.281、0.320、0.399.融合模型预测心脏重构的性能最优,AUC(95%CI)为0.967(0.847~0.983).结论:基于超声心动图影像组学数据的融合模型能够实现对 HCM 患者心脏重构的准确预测.
Aim:To explore the predictive value of radiomics from ultrasonic cardiography data for cardiac remodeling in patients with hypertrophic cardiomyopathy(HCM).Methods:A total of 265 patients with HCM who were treated in the Affiliated Hospital of Chengdu University and West China Fourth Hospital From January 1,2021 to December 31,2024,were selected,and 92 patients had cardiac remodeling.Logistic regression was used to screen factors influencing cardiac re-modeling and construct a clinical model.Apical four-chamber cardiac images were retained to extract shape,first-order,tex-ture,and wavelet features,and LASSO regression and 10-fold cross-validation were used to adjust parameters and select the optimal features;based on these optimal features,a radiomics model was constructed using linear regression.Using the opti-mal features as predictors,models were separately built with the k-nearest neighbor algorithm,multilayer neural network al-gorithm,and support vector machine(SVM)algorithm,and the model with the best performance was selected as the final deep learning model through ROC curve analysis.Based on the 3 models,a fusion model employing a weighted integration strategy was constructed.The performance of each model was evaluated using ROC curves combined with the Bootstrap 1 000 method.Results:The clinical model included 6 predictive factors:LAD,LVPWd,E/A,LVRI,MVCF,and LVMI.The radiomics model included 5 optimal features:wavelet_HLH_gray-level size zone matrix_zone percentage,wavelet_HHH_gray-level size zone matrix_zone size non-uniformity,wavelet_LHL_gray-level dependence matrix_dependence variance,gra-dient_gray-level size zone matrix_zone variance,and wavelet_LLH_gray-level dependence matrix_dependence non-uniformi-ty.The SVM model was selected as the deep learning model.The weights of the 3 models in the fusion model were 0.281,0.320,and 0.399,respectively.The fusion model showed the best performance in predicting cardiac remodeling,with an AUC(95%CI)of 0.967(0.847-0.983).Conclusion:The fusion model based on ultrasound radiomics can accurately predict cardiac remodeling in patients with HCM.
李建利;杨淑娟;曾红莲;杨波;冯坤
成都大学附属医院体检中心 成都 610081四川大学华西公共卫生学院/华西第四医院健康行为与社会医学系 成都 610041成都大学附属医院体检中心 成都 610081成都大学附属医院体检中心 成都 610081成都大学附属医院心功能室 成都 610081
医药卫生
肥厚性心肌病心脏重构超声影像组学超声心动图
hypertrophic cardiomyopathycardiac remodelingultrasound radiomicsultrasonic cardiography
《郑州大学学报(医学版)》 2026 (3)
86-90,5
四川省科技计划项目(2023YFS0251)
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