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基于深度学习的RCA PCAT和CT-FFR以外的斑块体积的预后价值在植入支架的患者中

Zengfa Huang¹, Ruiyao Tang¹, Xinyu Du^1,2

¹Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China.

Current medical imaging

|May 13, 2025

概括

冠状动脉CT血管学对冠状动脉脂肪组织衰减 (PCAT) 和冠状动脉CT血管学斑块体积的深度学习分析预测穿皮冠状动脉干预后患者的主要不良心血管事件,超越CT-FFR.

科学领域:

心血管成像 - 心血管成像
人工智能在医学中的应用
干预心脏病学干预心脏病学

背景情况:

冠状动脉计算机断层扫描血管造影 (CTA) 对于评估冠状动脉疾病至关重要.
从CT (CT-FFR) 导出的分量流量储备 (FFR) 评估了病变严重程度,但其皮肤穿刺冠状动脉干预 (PCI) 后的预后值需要进一步评估.
冠周脂肪组织 (PCAT) 衰减和斑块体积是新兴的成像生物标志物.

研究的目的:

与CT-FFR相比,评估基于深度学习的PCAT减弱和斑块体积的预后意义.
确定这些成像参数是否预测PCI患者的重大心血管不良事件 (MACE).

主要方法:

对183名患有PCI且接受冠状动脉CTA的患者进行了回顾性分析.
用于量化PCAT衰减和斑块体积的人工智能辅助工作站.
卡普兰-梅尔分析和多变量考克斯回归用于评估MACE (非致命性心脏病发作,中风,死亡率).

主要成果:

在随访期间,共发生了22个MACE.
右冠状动脉 (RCA) 的PCAT衰减和斑块体积与MACE增加显著相关 (分别为p=0.007和p=0.008).
RCA PCAT和斑块体积是MACE的独立预测因素 (HR:2.94,p=0.025和HR:3.91,p=0.024),而CT-FFR则不是 (p=0.271).

结论:

基于深度学习的RCA PCAT衰减和冠状动脉CTA斑块体积的分析提供了显著的预后价值.
在PCI后的患者中,这些参数与MACE比CT-FFR更强烈地相关.
人工智能驱动的成像生物标志物对接受PCI的患者的风险分层有希望.

关键词:

冠状动脉计算机断层扫描血管图谱.通过皮肤进行冠状动脉干预.周冠状腺脂肪组织衰减计算断层扫描.预后. 预后. 在预后.

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