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基于冠状动脉计算机断层扫描血管造影的冠状动脉周围脂肪组织放射组学可识别易损斑块。

Pericoronary Adipose Tissue Radiomics from Coronary Computed Tomography Angiography Identifies Vulnerable Plaques.

作者信息

Kim Justin N, Gomez-Perez Lia, Zimin Vladislav N, Makhlouf Mohamed H E, Al-Kindi Sadeer, Wilson David L, Lee Juhwan

机构信息

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.

Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA.

出版信息

Bioengineering (Basel). 2023 Mar 14;10(3):360. doi: 10.3390/bioengineering10030360.

Abstract

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.

摘要

计算机断层扫描(CT)显示的冠状动脉周围脂肪组织(PCAT)特征已被证明可反映局部炎症和心血管风险增加。我们的目标是确定从冠状动脉CT血管造影(CCTA)图像中提取的PCAT放射组学特征是否与血管内光学相干断层扫描(IVOCT)识别的易损斑块特征(如微通道(MC)和薄帽纤维粥样斑块(TCFA))相关。对25例患者的30个病变的CCTA和IVOCT图像进行了配准。从配准后的IVOCT图像中识别出具有易损斑块的血管。从CCTA图像中提取病变感兴趣区域(PCAT-LOI)和整个血管(PCAT-Vessel)的PCAT放射组学特征。我们提取了1356个放射组学特征,包括强度(一阶)、形状和纹理特征。使用标准方法(如高特征相关性)对特征进行降维。通过1000次重复的分层三折交叉验证,我们确定了CCTA中PCAT放射组学特征预测IVOCT易损斑块特征的能力。在识别TCFA病变方面,PCAT-LOI和PCAT-Vessel放射组学模型表现相当(曲线下面积(AUC)±标准差为0.78±0.13、0.77±0.14)。对于识别MC病变,PCAT-Vessel放射组学模型(0.89±0.09)的相关性略优于PCAT-LOI模型(0.83±0.12)。此外,PCAT-LOI和PCAT-Vessel放射组学模型都以类似的标准识别出被认为高度易损的冠状动脉血管(即同时存在TCFA和MC;0.88±0.10、0.91±0.09)。最有利的放射组学特征往往是描述PCAT纹理和大小的特征。PCAT放射组学的应用可以识别出与IVOCT一致的具有TCFA或MC的冠状动脉血管。此外,使用CCTA放射组学可以通过无创检测仅IVOCT可见的易损斑块特征来改善风险分层。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7082/10045206/a23616749305/bioengineering-10-00360-g001.jpg

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