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基于深度学习的大型多中心注册中心中特定血管的冠状动脉钙化自动定量分析。

Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.

机构信息

British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.

Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Floor 4, Los Angeles 90048 CA, USA.

出版信息

Eur Heart J Cardiovasc Imaging. 2024 Jun 28;25(7):976-985. doi: 10.1093/ehjci/jeae045.

DOI:10.1093/ehjci/jeae045
PMID:38376471
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11210989/
Abstract

AIMS

Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram (ECG) gated and attenuation correction (AC) computed tomography (CT) in a large multi-centre registry.

METHODS AND RESULTS

Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX), and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated AC CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400, and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4 ± 1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and AC CT [0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing AC CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, P < 0.001) and AC CT (HR 4.21, 95% CI 3.48, 5.08, P < 0.001).

CONCLUSION

Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and AC CT and provides important prognostic information.

摘要

目的

血管特异性冠状动脉钙化(CAC)对预后评估具有附加价值。我们评估了在大型多中心注册研究中,基于心电图(ECG)门控和衰减校正(AC)计算机断层扫描(CT)的深度学习(DL)自动血管特异性 CAC 分析的准确性和预后意义。

方法和结果

使用在 3000 次门控 CT 上训练并在 2094 次门控 CT 和 5969 次非门控 AC CT 上测试的 DL 模型评估左主干/左前降支(LM/LAD)、左回旋支(LCX)和右冠状动脉(RCA)的血管特异性 CAC。使用线性加权 Cohen's Kappa 评估 CAC 零、1-100、101-400 和 >400 单位(AU)的血管特异性一致性。在 2.4±1.4 年的随访期间评估主要不良心血管事件(MACE)的风险,使用风险比(HR)和 95%置信区间(CI)。DL 与专家地面真实之间在门控 CT 上的 LM/LAD、LCX 和 RCA 的 CAC 之间具有很强到极好的一致性[0.90(95%CI 0.89 至 0.92);0.70(0.68 至 0.73);0.79(0.77 至 0.81)]和 AC CT[0.78(0.77 至 0.80);0.60(0.58 至 0.62);0.70(0.68 至 0.71)]。在接受门控 CT 的 242 例(12%)和接受 AC CT 的 841 例(14%)中发生了 MACE。LM/LAD CAC >400 AU 与门控 CT(HR 12.0,95%CI 7.96,18.0,P <0.001)和 AC CT(HR 4.21,95%CI 3.48,5.08,P <0.001)的最高 MACE 风险相关。

结论

基于 DL 的血管特异性 CAC 评估可以在门控 CT 和 AC CT 上准确快速地进行,并提供重要的预后信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1370/11210989/5e221b500239/jeae045_ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1370/11210989/5e221b500239/jeae045_ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1370/11210989/5e221b500239/jeae045_ga1.jpg

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