Cho Sang-Geon, Lee Jong Eun, Cho Kyung Hoon, Park Ki-Seong, Kim Jahae, Moon Jang Bae, Kim Kang Bin, Kim Ju Han, Song Ho-Chun
Department of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
Eur J Nucl Med Mol Imaging. 2025 Feb;52(3):1050-1059. doi: 10.1007/s00259-024-06948-8. Epub 2024 Oct 15.
This study aimed to test whether the coronary artery calcium (CAC) burden on attenuation correction computed tomography (CTac), measured using artificial intelligence (AI-CACac), correlates with coronary flow capacity (CFC) and prognosis.
We retrospectively enrolled patients who underwent [N]ammonia positron emission tomography (PET) between September 2021 and May 2023. CTac data were obtained from all the patients. Patients with (history of) acute coronary syndrome, previous coronary stent insertion or bypass surgery, or left ventricular ejection fraction < 40% were excluded. The total Agatston score measured using a dedicated AI-CAC quantification software on CTac was defined as AI-CACac. The correlations between AI-CACac and PET-measured myocardial blood flow (MBF) and CFC and significant ischaemia (summed difference score ≥ 7) were analysed. Their prognostic values for major cardiovascular events (MACE), including death, nonfatal myocardial infarction, hospitalisation due to angina pectoris or heart failure, and late (> 90 days) revascularisation, were also evaluated.
In total, 289 patients were included in this study. Significant negative correlations were found between AI-CACac and stress MBF (ρ = -0.363, p < 0.001) and MFR (ρ = -0.305, p < 0.001). AI-CACac > 10 was associated with a significantly higher prevalence of impaired CFC (31% vs. 7%, p < 0.001) and significant ischaemia (20% vs. 7%), which remained significant after adjusting for other risk factors. MACE occurred in 49 (17%) patients (median follow-up, 284 days), and those who experienced MACE had significantly higher AI-CACac (median, 166 vs. 56; p = 0.039). However, multivariable analysis revealed an independent prognostic association among impaired CFC, diabetes, smoking, but not for AI-CACac.
AI-measured CACac correlates well with PET-measured MBF and CFC, but its prognostic significance requires further validation.
本研究旨在测试使用人工智能测量的衰减校正计算机断层扫描（CTac）上的冠状动脉钙化（CAC）负荷是否与冠状动脉血流容量（CFC）和预后相关。
我们回顾性纳入了2021年9月至2023年5月期间接受[N]氨正电子发射断层扫描（PET）的患者。所有患者均获取了CTac数据。排除有急性冠状动脉综合征病史、既往冠状动脉支架置入或搭桥手术史或左心室射血分数<40%的患者。使用专门的人工智能CAC量化软件在CTac上测量的总阿加斯顿评分定义为AI-CACac。分析AI-CACac与PET测量的心肌血流量（MBF）、CFC以及显著缺血（总和差异评分≥7）之间的相关性。还评估了它们对主要心血管事件（MACE）的预后价值，包括死亡、非致命性心肌梗死、因心绞痛或心力衰竭住院以及晚期（>90天）血运重建。
本研究共纳入289例患者。发现AI-CACac与应激MBF（ρ = -0.363，p < 0.001）和MFR（ρ = -0.305，p < 0.001）之间存在显著负相关。AI-CACac>10与CFC受损的患病率显著更高（31%对7%，p < 0.001）和显著缺血（20%对7%）相关，在调整其他危险因素后仍具有显著性。49例（17%）患者发生了MACE（中位随访时间，284天），发生MACE的患者AI-CACac显著更高（中位数，166对56；p = 0.039）。然而，多变量分析显示CFC受损、糖尿病、吸烟之间存在独立的预后关联，但AI-CACac不存在。
人工智能测量的CACac与PET测量的MBF和CFC相关性良好，但其预后意义需要进一步验证。
