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

• Published in: JACC. Cardiovascular imaging 2023-05, Vol.16 (5), p.675-687
• Main Authors: Pieszko, Konrad, Shanbhag, Aakash, Killekar, Aditya, Miller, Robert J.H., Lemley, Mark, Otaki, Yuka, Singh, Ananya, Kwiecinski, Jacek, Gransar, Heidi, Van Kriekinge, Serge D., Kavanagh, Paul B., Miller, Edward J., Bateman, Timothy, Liang, Joanna X., Berman, Daniel S., Dey, Damini, Slomka, Piotr J.
• Format: Article
• Language: English
• Subjects: attenuation correction; Calcium; computed tomography; coronary artery disease; Coronary Artery Disease - diagnostic imaging; coronary calcium score; Deep Learning; Humans; positron emission tomography; Positron Emission Tomography Computed Tomography; Predictive Value of Tests
• ISSN: 1936-878X; 1876-7591
• DOI: 10.1016/j.jcmg.2022.06.006

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