How early can atherosclerosis be detected by coronary CT angiography? Insights from quantitative CT analysis of serial scans in the PARADIGM trial

Rhanderson Cardoso, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: rcardoso2@bwh.harvard.edu.
Andrew D. Choi, Department of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
Arthur Shiyovich, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Stephanie A. Besser, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
James K. Min, Cleerly Inc, New York, NY, USA.
James Earls, Cleerly Inc, New York, NY, USA.
Daniele Andreini, Division of Cardiology and Cardiac Imaging, IRCCS Ospedale Galeazzi Sant'Ambrogio, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
Mouaz H. Al-Mallah, Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA.
Matthew J. Budoff, Department of Medicine, Lundquist Institute at Harbor-UCLA, Torrance, CA, USA.
Filippo Cademartiri, Fondazione Monasterio/CNR, Pisa, Italy.
Kavitha Chinnaiyan, Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA.
Jung Hyun Choi, Busan University Hospital, Busan, South Korea.
Eun Ju Chun, Seoul National University Bundang Hospital, Sungnam, South Korea.
Edoardo Conte, Centro Cardiologico Monzino, IRCCS, Milan, Italy.
Ilan Gottlieb, Department of Radiology, Casa de Saude Sao Jose, Rio de Janeiro, Brazil.
Martin Hadamitzky, Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany.
Yong-Jin Kim, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
Byoung Kwon Lee, Gangnam Severance Hospital, Younsei University College of Medicine, Seoul, South Korea.
Jonathon A. Leipsic, Department of Radiology, St. Paul's Hospital, University of British Columbia, Vancouver, Canada.
Erica Maffei, IRCCS SYNLAB SDN, Naples, Italy.
Hugo Marques, Hospital da Luz, Lisbon, Portugal.
Pedro de Araújo Gonçalves, Hospital da Luz, Lisbon, Portugal.
Gianluca Pontone, Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
Sang-Eun Lee, Division of Cardiology, Department of Internal Medicine, Ewha Womans University, Seoul, South Korea.
Ji Min Sung, CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea.
Renu Virmani, Department of Pathology, CVPath Institute, Gaithersburg, MD, USA.
Habib Samady, Georgia Heart Institute, Northeast Georgia Health System, Gainesville, GA, USA.
Fay Y. Lin, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Peter H. Stone, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Daniel S. Berman, Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, CA, USA.
Jagat Narula, University of Texas Health Houston, Houston, TX, USA.
Leslee J. Shaw, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Document Type

Journal Article

Publication Date

1-1-2023

Journal

Journal of cardiovascular computed tomography

Volume

17

Issue

6

DOI

10.1016/j.jcct.2023.08.012

Keywords

Artificial intelligence; Atherosclerosis; Coronary; Coronary CT angiography; Small plaque

Abstract

BACKGROUND: Non-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown. METHODS: We performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 ​mm. RESULTS: A total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 ​mm (IQR 3.5-13.9 ​mm), most of which was non-calcified (median 6.2 ​mm; 2.9-12.3 ​mm). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 ​± ​1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 ​mm (IQR 8.3-45.2 ​mm) and 13.8 ​mm (IQR 5.7-33.4 ​mm), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up. CONCLUSION: In this retrospective analysis from the PARADIGM study, small plaques (<50 ​mm) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA.

Department

Medicine

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