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

Konrad Pieszko; Aakash Shanbhag; Aditya Killekar; Robert J H Miller; Mark Lemley; Yuka Otaki; Ananya Singh; Jacek Kwiecinski; Heidi Gransar; Serge D Van Kriekinge; Paul B Kavanagh; Edward J Miller; Timothy Bateman; Joanna X Liang; Daniel S Berman; Damini Dey; Piotr J Slomka

doi:10.1016/j.jcmg.2022.06.006

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

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

Authors

Konrad Pieszko¹, Aakash Shanbhag², Aditya Killekar², Robert J H Miller³, Mark Lemley², Yuka Otaki², Ananya Singh², Jacek Kwiecinski⁴, Heidi Gransar², Serge D Van Kriekinge², Paul B Kavanagh², Edward J Miller⁵, Timothy Bateman⁶, Joanna X Liang², Daniel S Berman², Damini Dey², Piotr J Slomka⁷

Affiliations

¹ 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.
² Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
³ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada.
⁴ 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 Angiology, Institute of Cardiology, Warsaw, Poland.
⁵ Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
⁶ Cardiovascular Imaging Technologies, Kansas City, Missouri, USA.
⁷ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: piotr.slomka@cshs.org.

PMID: 36284402
DOI: 10.1016/j.jcmg.2022.06.006

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.

Keywords: attenuation correction; computed tomography; coronary artery disease; coronary calcium score; positron emission tomography.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Calcium
Coronary Artery Disease* / diagnostic imaging
Deep Learning*
Humans
Positron Emission Tomography Computed Tomography
Predictive Value of Tests

Substances

Calcium

Grants and funding

R01 HL089765/HL/NHLBI NIH HHS/United States