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首页> 美国卫生研究院文献>Journal of Clinical Medicine >Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension
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Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension

机译：基于心肌MRI纹理评估的肺动脉瘤的辐射瘤检测：肺动脉高压的机器学习模型比较 - 心脏MRI射线

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摘要

The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions.
机译：可靠性，非侵入性成像的基于肺动脉高压（pH）的作用仍然是诊断攻击。目前的导频射频研究的目的是评估心脏MRI（CMRI）的纹理特征的诊断性能，以准确预测pH值。该研究涉及IRB批准的CMRIS从72名患者（42 pH和30个健康对照）进行初步分析的回顾性分析。进行亚组分析，包括来自pH组的患者，左心室喷射分数≥50％。使用平衡的稳态自由进样（BSSFP）Cine短轴成像，从中左心室心肌产生纹理特征。评估了四十五种不同组合的分类器模型和特征选择技术。使用接收器操作特性曲线评估模型性能。使用完整特征集的多层的Perceptron模型拟合是最佳分类器模型，用于主要分析（AUC 0.862，精度78％）和子组分析（AUC 0.918，精度80％）。模型性能基于所选择的模型特征选择组合，展示了模型（AUC 0.523-0.918）之间的相当大的变化。基于心脏MRI的射线瘤使用质地特征的pH值是可行的，即使保存左心室喷射级分也是可行的。

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