Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
Int J Cardiovasc Imaging. 2024 Jun;40(6):1201-1209. doi: 10.1007/s10554-024-03087-x. Epub 2024 Apr 17.
This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.
这项研究评估了人工智能定量计算机断层扫描（AI-QCT）与多学会 2022 年 CAD-RADS 2.0 专家共识中编码的动脉粥样硬化疾病负担的定性方法的一致性。105 名因胸痛接受心脏计算机断层扫描血管造影（CCTA）的患者由一个盲法核心实验室进行评估，该实验室使用经过美国食品和药物管理局批准的软件（Cleerly，丹佛，CO）进行 AI-QCT，该软件通过人工智能分析狭窄程度百分比、斑块体积和斑块成分等因素。然后，根据最近验证的预后阈值对 AI-QCT 斑块体积进行分期，并通过专家共识将其与 CAD-RADS 2.0 斑块评估的临床方法（节段受累评分（SIS）、冠状动脉钙评分（CACS）、视觉评估和 CAD-RADS 狭窄程度百分比（%））进行比较，盲法核心实验室读取结果。受试者的平均年龄为 59±11 岁；44%为女性，根据专家共识，50%的患者为 CAD-RADS 1-2，21%为 CAD-RADS 3 及以上。AI-QCT 定量斑块负担分期与 SIS 的一致性非常好，达到 93%（k=0.87，95%CI：0.79-0.96）。AI-QCT 定量斑块体积与视觉评估的类别（64.4%；k=0.488[0.38-0.60]）和 CACS（66.3%；k=0.488[0.36-0.61]）之间存在中度一致性。AI-QCT 斑块体积分期与 CAD-RADS 狭窄程度分类之间的一致性也为中度。在小斑块体积时存在不一致性。随着进一步验证，这些结果表明 AI-QCT 作为一种快速、可重复的方法来量化总斑块负担具有潜力。
