To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiveness of CCTA for myocardial ischaemia patients was explored. This was a retrospective analysis of the CCTA images of 155 patients with clinically diagnosed coronary heart disease from September 2019 to January 2020, 79 of whom were considered positive (myocardial ischaemia) and 76 negative (normal myocardial blood supply) according to their clinical diagnoses. By using the deep learning model-based CQK software, the myocardium was automatically segmented from the CCTA images and used to extract texture features. All patients were randomly divided into a training cohort and a test cohort at a 7:3 ratio. The Spearman correlation and least absolute shrinkage and selection operator (LASSO) method were used for feature selection. Based on the selected features of the training cohort, a multivariable logistic regression model was established. Finally, the test cohort was used to verify the regression model. A total of 387 features were extracted from the CCTA images of the 155 coronary heart disease patients. After performing dimensionality reduction with the Spearman correlation and LASSO, three texture features were selected. The accuracy, area under the curve, specificity, sensitivity, positive predictive value and negative predictive value of the constructed multivariable logistic regression model with the test cohort were 0.783, 0.875, 0.733, 0.875, 0.650 and 0.769, respectively. CCTA imaging texture features of the myocardium have potential as biomarkers for diagnosing myocardial ischaemia.

中文翻译：

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基于CCTA心肌纹理特征的心肌缺血诊断有效性探索

为探讨冠状动脉粥样硬化性心脏病患者冠状动脉CT血管造影（CCTA）图像心肌纹理特征，建立分类模型，探讨CCTA对心肌缺血患者的诊断有效性。这是对 2019 年 9 月至 2020 年 1 月临床诊断为冠心病的 155 例患者的 CCTA 图像的回顾性分析，其中 79 例根据其临床诊断为阳性（心肌缺血）和 76 例阴性（心肌血供正常）。通过使用基于深度学习模型的 CQK 软件，从 CCTA 图像中自动分割心肌并用于提取纹理特征。所有患者以 7:3 的比例随机分为训练队列和测试队列。Spearman相关和最小绝对收缩和选择算子（LASSO）方法用于特征选择。基于选定的训练队列特征，建立多变量逻辑回归模型。最后，使用测试队列来验证回归模型。从 155 名冠心病患者的 CCTA 图像中总共提取了 387 个特征。使用 Spearman 相关和 LASSO 进行降维后，选择了三个纹理特征。构建的多变量逻辑回归模型与测试队列的准确度、曲线下面积、特异性、敏感性、阳性预测值和阴性预测值分别为0.783、0.875、0.733、0.875、0.650和0.769。