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<p>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&#xa0;and&#xa0;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.</p>

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Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Kenrick Schulze,
  • Anne-Marieke Stantien,
  • Michelle C. Williams,
  • Vassilios S. Vassiliou,
  • Andreas A. Giannopoulos,
  • Koen Nieman,
  • Pál Maurovich-Horvat,
  • Jason M. Tarkin,
  • Rozemarijn Vliegenthart,
  • Jonathan Weir-McCall,
  • Mahmoud Mohamed,
  • Bernhard Föllmer,
  • Federico Biavati,
  • Ann-Christine Stahl,
  • Jakob Knape,
  • Hanna Balogh,
  • Nicola Galea,
  • Ivana Išgum,
  • Armin Arbab-Zadeh,
  • Hatem Alkadhi,
  • Robert Manka,
  • David A. Wood,
  • Edward D. Nicol,
  • Nick S. Nurmohamed,
  • Fabrice M. A. C. Martens,
  • Damini Dey,
  • David E. Newby,
  • Marc Dewey

摘要

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.