Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.
Authors
Affiliations (28)
Affiliations (28)
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
- British Heart Foundation Centre of Research Excellence, University of Edinburgh, Edinburgh, UK.
- Department of Cardiac Medicine, Norwich Medical School, University of East Anglia, Norwich, England.
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
- Department of Medicine/Cardiovascular and Radiology, Stanford University, Stanford, CA, USA.
- Department of Radiology, Medical Imaging Center, Semmelweis University, Budapest, Hungary.
- Section of Cardiorespiratory Medicine, Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, UK.
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Cardiovascular Imaging, Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Rome, Italy.
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Institute for Biomedical Engineering, University and ETH, Zurich, Switzerland.
- National Heart and Lung Institute, Imperial College London, London, UK.
- National Institute for Prevention and Cardiovascular Health, University of Galway, Galway, Republic of Ireland.
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Department of Cardiology and Radiology, Royal Brompton Hospital, London, UK.
- Department of Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
- Departments of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Departments of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany. [email protected].
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. [email protected].
- Berlin University Alliance, Berlin, Germany. [email protected].
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany. [email protected].
- Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany. [email protected].
Abstract
Coronary CT angiography is widely implemented, with an estimated 2.2 million procedures in patients with stable chest pain every year in Europe alone. In parallel, artificial intelligence and machine learning are poised to transform coronary atherosclerotic plaque evaluation by improving reliability and speed. However, little is known about how to use coronary atherosclerosis imaging biomarkers to individualize recommendations for medical treatment. This Consensus Statement from the Quantitative Cardiovascular Imaging (QCI) Study Group outlines key recommendations derived from a three-step Delphi process that took place after the third international QCI Study Group meeting in September 2024. Experts from various fields of cardiovascular imaging agreed on the use of age-adjusted and gender-adjusted percentile curves, based on coronary plaque data from the DISCHARGE and SCOT-HEART trials. Two key issues were addressed: the need to harness the reliability and precision of artificial intelligence and machine learning tools and to tailor treatment on the basis of individualized plaque analysis. The QCI Study Group recommends that the presence of any atherosclerotic plaque should lead to a recommendation of pharmacological treatment, whereas the 70th percentile of total plaque volume warrants high-intensity treatment. The aim of these recommendations is to lay the groundwork for future trials and to unlock the potential of coronary CT angiography to improve patient outcomes globally.