Yin Kejie, Chen Wenping, Qin Guochu, Liang Jing, Bao Xue, Yu Hongming, Li Hui, Xu Jianhua, Chen Xingbiao, Wang Yujie, Savage Rock H, Schoepf U Joseph, Mu Dan, Zhang Bing
Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Department of Cardiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):5708-5720. doi: 10.21037/qims-24-247. Epub 2024 Jul 24.
The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm).
A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined.
A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively.
The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.
冠状动脉钙化积分（CACS）已被证明是心血管事件的独立预测指标。传统的冠状动脉钙化评分算法已针对心电图（ECG）门控图像进行了优化，这些图像是在特定设置和时间采集的。因此，如果基于人工智能的冠状动脉钙化积分（AI-CACS）能够从胸部低剂量计算机断层扫描（LDCT）检查中计算出来，那么它在提前评估冠状动脉疾病（CAD）风险方面可能具有重要价值，并且有可能降低患者心血管事件的发生率。本研究旨在评估AI-CACS算法在三种不同层厚（1、3和5毫米）的非门控胸部扫描中的性能。
本研究前瞻性纳入了135例同时接受胸部LDCT和ECG门控非增强心脏CT检查的患者。使用AI-CACS软件从层厚为1、3和5毫米重建的胸部CT图像中自动得出阿加斯顿积分。然后将这些积分与使用传统半自动方法从ECG门控心脏CT数据中获得的积分进行比较，该传统方法作为参考。分析了AI-CACS与心电图门控冠状动脉钙化积分（ECG-CACS）之间的相关性，并使用布兰德-奥特曼图评估一致性。基于计算出的CACS进行风险分层，并确定一致性率。
最终分析纳入了112例患者。三种不同层厚（1、3和5毫米）的AI-CACS与ECG-CACS之间的相关性分别为0.973、0.941和0.834（均P<0.01）。布兰德-奥特曼图显示，三种层厚的AI-CACS平均差异分别为-6.5、15.4和53.1。三个AI-CACS组的风险类别一致性分别为0.868、0.772和0.412（均P<0.01）。而一致性率分别为91%、84.8%和62.5%。
基于人工智能的算法成功地从胸部LDCT扫描中计算出CACS，证明了其在风险分类中的实用性。此外，从层厚为1毫米的图像得出的CACS比从层厚为3毫米和5毫米的图像得出的更准确。
