Huang Zengfa, Tang Ruiyao, Du Xinyu, Ding Yi, Yang ZhiWen, Cao Beibei, Li Mei, Wang Xi, Wang Wanpeng, Li Zuoqin, Xiao Jianwei, Wang Xiang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China.
Department of Radiology, The Central Hospital of Wuhan Base, Hubei University of Medicine, Shiyan, Hubei, 442000, China.
Curr Med Imaging. 2025;21:e15734056335065. doi: 10.2174/0115734056335065250426150739.
The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).
A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.
In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 7.05, 95%CI: 1.44-34.63, p = 0.016) and plaque volume (HR: 3.84, 95%CI: 1.44-10.27, p = 0.007) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.150).
Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CTFFR in patients with PCI.
本研究旨在探讨基于深度学习的冠状动脉周围脂肪组织衰减计算机断层扫描（PCAT）和斑块体积在经皮冠状动脉介入治疗（PCI）患者中超越冠状动脉计算机断层扫描血管造影（CTA）衍生的血流储备分数（CT-FFR）的预后价值。
本回顾性研究纳入了183例行冠状动脉CTA检查的PCI患者。影像评估包括PCAT、斑块体积和CT-FFR，这些均使用人工智能（AI）辅助工作站进行。采用Kaplan-Meier生存曲线分析和多变量Cox回归来评估主要不良心血管事件（MACE），包括非致命性心肌梗死（MI）、中风和死亡率。
在中位随访期38.0个月（34.6 - 54.6个月）内，共发生22例（12%）MACE。Kaplan-Meier分析显示，右冠状动脉（RCA）的PCAT（p = 0.007）和斑块体积（p = 0.008）与MACE的增加显著相关。多变量Cox回归表明，经临床危险因素调整后，RCA的PCAT（风险比（HR）：7.05，95%置信区间（CI）：1.44 - 34.63，p = 0.016）和斑块体积（HR：3.84，95%CI：1.44 - 10.27，p = 0.007）是MACE的独立预测因素。然而，在多变量Cox回归中，CT-FFR与MACE无独立相关性（p = 0.150）。
在PCI患者中，基于深度学习的冠状动脉CTA得出的RCA的PCAT和斑块体积与MACE的相关性比CT-FFR更强。
