Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

Journal: The international journal of cardiovascular imaging
PMID: 38630211

Abstract

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Authors

  • Hufsa Khan
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Kopal Bansal
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • William F Griffin
    Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Catherine Cantlay
    Cardiovascular and Metabolic Sciences, Lerner Research Institute (M.M., C.C., N.K., M.K.C.), Diagnostic Radiology, Cleveland Clinic.
  • Alfateh Sidahmed
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Nick S Nurmohamed
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Robert K Zeman
    Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Richard J Katz
    Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
  • Ron Blankstein
    Department of Medicine (Cardiovascular), Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • James P Earls
    Cleerly inc., New York, United States.
  • Andrew D Choi
    Division of Cardiology and Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA adchoi@mfa.gwu.edu.

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