深度学习冠状动脉 CT 衰减图中的钙评分可准确预测不良心血管事件。

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

机构信息

Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Góra, Zielona Góra, Poland.

Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.

出版信息

JACC Cardiovasc Imaging. 2023 May;16(5):675-687. doi: 10.1016/j.jcmg.2022.06.006. Epub 2022 Sep 14.

DOI:10.1016/j.jcmg.2022.06.006

PMID:36284402

Abstract

BACKGROUND

Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging.

OBJECTIVES

The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans.

METHODS

The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans.

RESULTS

Automatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (-0.02; 95% CI: -0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19).

CONCLUSIONS

DL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.

摘要

背景

通过计算机断层扫描（CT）成像评估冠状动脉钙（CAC）可提供动脉粥样硬化负担的准确测量。CAC 在 CT 衰减校正（CTAC）扫描中也可见，该扫描总是与心脏正电子发射断层扫描（PET）成像一起获得。

目的

本研究旨在开发一种深度学习（DL）模型，能够从 PET CTAC 扫描中自动定义 CAC。

方法

最初为视频应用开发的新型 DL 模型被改编为快速定量 CAC。该模型使用 9543 个专家注释的 CT 扫描进行训练，并在接受 PET/CT 成像且存在重大不良心脏事件（MACE）的 4331 名患者中进行了测试（随访 4.3 年），包括在 2737 名患者中可获得的当天配对心电图门控 CAC 扫描。分析了 4 个 CAC 评分类别（0、1-100、101-400 和>400）的 MACE 风险分层，并比较了由专家观察者从心电图门控 CT 扫描中得出的 CAC 评分（标准评分）与来自 CTAC 扫描的自动 DL 评分。

结果

自动 DL 评分每扫描不到 6 秒。DL CTAC 评分在 CAC 评分类别中逐步增加了 MACE 的风险（HR 高达 3.2；P<0.001）。标准 CAC 评分与 DL CTAC 评分相比，净重新分类改善无统计学意义（-0.02；95%CI：-0.11 至 0.07）。标准（85%）和 DL CTAC（83%）CAC 评分为零 CAC 的阴性预测值相似（P=0.19）。