Verpalen Victor A, Coerkamp Casper F, Henriques José P S, Isgum Ivana, Planken R Nils
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Eur Radiol. 2025 Mar;35(3):1543-1551. doi: 10.1007/s00330-024-11308-z. Epub 2025 Jan 10.
The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
This single-center retrospective study included 50 patients that underwent CCTA to rule out obstructive coronary artery disease between 2017-2022. Two expert CCTA readers and CorEx-2.0 independently assessed all 150 vessels using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Inter-reader agreement analysis and diagnostic performance of CorEx-2.0, compared with each expert reader as references, were evaluated using percent agreement, Cohen's kappa for the binary CAD-RADS classification (CAD-RADS 0-3 versus 4-5) at patient level, and linearly weighted kappa for the 6-group CAD-RADS classification at vessel level.
Overall, 50 patients and 150 vessels were evaluated. Inter-reader agreement using the binary classification at patient level was 91.8% (45/49) with a Cohen's kappa of 0.80. For the 6-group classification at vessel level, inter-reader agreement was 67.6% (100/148) with a linearly weighted kappa of 0.77. CorEx-2.0 showed 100% sensitivity for detecting CAD-RADS ≥ 4 and kappa values of 0.86 versus both readers using the binary classification at patient level. For the 6-group classification at vessel level, CorEx-2.0 demonstrated weighted kappa values of 0.71 versus reader 1 and 0.73 versus reader 2.
CorEx-2.0 identified all patients with severe stenosis (CAD-RADS ≥ 4) compared with expert readers and approached expert reader performance at vessel level (weighted kappa > 0.70).
Question Can deep learning models improve objectivity in coronary stenosis grading and reporting as coronary CT angiography (CTA) workloads rise? Findings The deep learning model (CorEx-2.0) identified all patients with severe stenoses when compared with expert readers and approached expert reader performance at vessel level. Clinical relevance CorEx-2.0 is a reliable tool for identifying patients with severe stenoses (≥ 70%), underscoring the potential of using this deep learning model to prioritize coronary CTA reading by flagging patients at risk of severe obstructive coronary artery disease.
使用深度学习模型对冠状动脉计算机断层扫描血管造影（CCTA）进行定量测量，可能会减少阅片者之间的差异，并提高临床报告的效率。本研究旨在调查一种最近更新的深度学习模型（CorEx-2.0）在量化冠状动脉狭窄方面的诊断性能，并分别与两位CCTA专家阅片者作为参考进行比较。
这项单中心回顾性研究纳入了2017年至2022年间接受CCTA以排除阻塞性冠状动脉疾病的50例患者。两位CCTA专家阅片者和CorEx-2.0使用冠状动脉疾病报告和数据系统（CAD-RADS）对所有150条血管进行独立评估。使用一致性百分比、患者水平上二元CAD-RADS分类（CAD-RADS 0-3与4-5）的Cohen's kappa以及血管水平上6组CAD-RADS分类的线性加权kappa，评估CorEx-2.0与每位专家阅片者作为参考相比的阅片者间一致性分析和诊断性能。
总体而言，评估了50例患者和150条血管。患者水平上使用二元分类的阅片者间一致性为91.8%（45/49），Cohen's kappa为0.80。对于血管水平上的6组分类，阅片者间一致性为67.6%（100/148），线性加权kappa为0.77。CorEx-2.0在检测CAD-RADS≥4时显示出100%的敏感性，在患者水平上使用二元分类时与两位阅片者相比kappa值为0.86。对于血管水平上的6组分类，CorEx-2.0与阅片者1相比加权kappa值为0.71，与阅片者2相比为0.73。
与专家阅片者相比，CorEx-2.0识别出了所有严重狭窄（CAD-RADS≥4）的患者，并在血管水平上接近专家阅片者的表现（加权kappa>0.70）。
问题随着冠状动脉CT血管造影（CTA）工作量的增加，深度学习模型能否提高冠状动脉狭窄分级和报告的客观性？发现深度学习模型（CorEx-2.0）与专家阅片者相比识别出了所有严重狭窄的患者，并在血管水平上接近专家阅片者的表现。临床意义CorEx-2.0是识别严重狭窄（≥70%）患者的可靠工具，强调了使用这种深度学习模型通过标记有严重阻塞性冠状动脉疾病风险的患者来优先进行冠状动脉CTA阅片的潜力。
