Jingping Wu, Xiao Meng, Dan Wu, Yuwei Li, Xinghua Zhang, Zhenping Wang, Xue Wang, Fan Zhang
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Significant and relevant FRP was screened by random forest algorithm based on machine learning, and then 3 models-VF (FAI and volume), FRP, and FRPC (FRP and clinical factors)-were then compared. Among AF individuals, the FRP and FRPC scores of persistent AF (PerAF, n = 44) and paroxysmal AF (PAF, n = 93) were compared with boxplot.</p><p><strong>Results: </strong>In the test cohort, FRP score demonstrated excellent distinctive ability in identifying AF, with an area under the curve (AUC) of 0.89, compared with the model incorporating FAI and volume (AUC = 0.83). The FRPC model, which combined FRP with clinical factors, showed an improved AUC of 0.98. 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引用次数: 0
摘要
目的:心外膜脂肪组织(EAT)与心房颤动(AF)有关。我们试图探讨FAI(脂肪注意指数),体积,冠状动脉周围脂肪组织(PCAT)的脂肪放射谱(FRP)在冠状动脉计算机断层血管造影(CCTA)中确定房颤存在和区分房颤类型的作用。方法:本研究回顾性纳入300例行CCTA的患者,并将其分为房颤组(n = 137)和非房颤组(n = 163)。在PCAT分割后,挖掘并测量FAI、volume和FRP的成像参数。每条冠状动脉提取853个放射组,共收集2559个放射组。采用基于机器学习的随机森林算法筛选具有显著性和相关性的FRP,然后比较VF (FAI和体积)、FRP和FRPC (FRP和临床因素)三种模型。在房颤个体中,用箱线图比较持续性房颤(PerAF, n = 44)和阵发性房颤(PAF, n = 93)的FRP和FRPC评分。结果:在测试队列中,与纳入FAI和体积的模型(AUC = 0.83)相比,FRP评分对AF的识别能力较好,AUC为0.89,结合临床因素,FRPC模型的AUC为0.98。在房颤类型中,PerAF患者的FPR和FRPC评分明显高于PAF患者(p结论:冠状动脉CTA上PCAT的结构放射学特征可检测与房颤相关的微观病理生理信息,有助于识别和区分房颤,并提供有希望的成像靶点。知识进展:分析冠状动脉周围的心外膜组织有助于识别和区分心房颤动及其类型。冠状动脉周围脂肪放射谱可以为房颤提供无创工具。
Radiomic phenotype of peri-coronary adipose tissue as a potential non-invasive imaging tool for detecting atrial fibrillation.
Objectives: Epicardial adipose tissue (EAT) contributes to atrial fibrillation (AF). We sought to explore the role of fat attention index (FAI), volume, and fat radiomic profile (FRP) of peri-coronary artery adipose tissue (PCAT) on coronary computed tomography angiography (CCTA) in determining the presence of AF and differentiating its types.
Methods: This study enrolled 300 patients who underwent CCTA retrospectively and divided them into AF (n = 137) and non-AF (n = 163) groups. The imaging parameters of FAI, volume, and FRP were excavated and measured after PCAT segmentation. Every coronary artery extracted 853 radiomics and a total of 2559 radiomics were collected. Significant and relevant FRP was screened by random forest algorithm based on machine learning, and then 3 models-VF (FAI and volume), FRP, and FRPC (FRP and clinical factors)-were then compared. Among AF individuals, the FRP and FRPC scores of persistent AF (PerAF, n = 44) and paroxysmal AF (PAF, n = 93) were compared with boxplot.
Results: In the test cohort, FRP score demonstrated excellent distinctive ability in identifying AF, with an area under the curve (AUC) of 0.89, compared with the model incorporating FAI and volume (AUC = 0.83). The FRPC model, which combined FRP with clinical factors, showed an improved AUC of 0.98. Among AF types, FRP and FRPC scores are significantly higher in the PerAF than PAF patients (P < .001) and 20 most contributive features were selected in identifying AF.
Conclusion: Textural radiomic features derived from PCAT on coronary CTA detect micro-pathophysiological information associated with AF, which may help identify and differentiate AF and provide a hopeful imaging target.
Advances in knowledge: The analysis of epicardial tissue around coronary arteries helps identify and differentiate atrial fibrillation and its types. Fat radiomic profiles derived from peri-coronary arteries fat could provide a non-invasive tool for atrial fibrillation.
期刊介绍:
BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences.
Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896.
Quick Facts:
- 2015 Impact Factor – 1.840
- Receipt to first decision – average of 6 weeks
- Acceptance to online publication – average of 3 weeks
- ISSN: 0007-1285
- eISSN: 1748-880X
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