PDF(Pubmed)
Abstract：
BACKGROUND: Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients.
METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.
RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.
CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.
METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.
RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.
CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.
摘要：
背景：糖尿病是一种常见的慢性代谢性疾病。该疾病的进展促进血管炎症和动脉粥样硬化的形成，导致心血管疾病。基于CCTA的冠状动脉血管周围脂肪组织衰减指数是一种新的非侵入性成像生物标志物，可以反映CCTA图像中血管周围脂肪组织衰减的空间变化和冠状动脉周围的炎症。在这项研究中,提出了一种影像组学方法,以高通量方式从CCTA中提取大量图像特征,并结合临床诊断数据,探索基于CCTA的血管周围脂肪成像数据对糖尿病患者冠心病的预测能力。
方法：采用R语言进行统计分析，筛选出差异显著的变量。预分离模型用于CCTA血管分割，筛选出冠状动脉周围脂肪区域。PyRadiomics用于计算冠状动脉周围脂肪组织的影像组学特征，和SVM，使用DT和RF对临床数据和影像组学数据进行建模和分析。使用PPV、FPR,AAC,ROC。
结果：结果表明，年龄存在显着差异，血压,糖尿病患者和无冠心病患者之间的一些生化指标。在1037个计算的放射学参数中，18.3%的人在成像组学特征上表现出显著差异。三种建模方法用于分析不同的临床信息组合，内部血管影像组学信息和冠状动脉血管脂肪影像组学信息。结果表明，在不同的机器学习模型下，完整数据的数据集具有最高的ACC值。支持向量机方法表现出最好的特异性,灵敏度,和这个数据集的准确性。
结论：在这项研究中，将CCTA的临床数据和冠状动脉影像组学数据进行融合，以预测糖尿病患者冠心病的发生。这为糖尿病患者早期发现冠心病提供了信息，并可以及时进行干预和治疗。
方法：采用R语言进行统计分析，筛选出差异显著的变量。预分离模型用于CCTA血管分割，筛选出冠状动脉周围脂肪区域。PyRadiomics用于计算冠状动脉周围脂肪组织的影像组学特征，和SVM，使用DT和RF对临床数据和影像组学数据进行建模和分析。使用PPV、FPR,AAC,ROC。
结果：结果表明，年龄存在显着差异，血压,糖尿病患者和无冠心病患者之间的一些生化指标。在1037个计算的放射学参数中，18.3%的人在成像组学特征上表现出显著差异。三种建模方法用于分析不同的临床信息组合，内部血管影像组学信息和冠状动脉血管脂肪影像组学信息。结果表明，在不同的机器学习模型下，完整数据的数据集具有最高的ACC值。支持向量机方法表现出最好的特异性,灵敏度,和这个数据集的准确性。
结论：在这项研究中，将CCTA的临床数据和冠状动脉影像组学数据进行融合，以预测糖尿病患者冠心病的发生。这为糖尿病患者早期发现冠心病提供了信息，并可以及时进行干预和治疗。
