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Objectives <p>Automated artificial intelligence (AI)-based assessment of atherosclerosis burden applied to coronary computed tomography angiography (CCTA) can optimize image processing times, standardize interpretation, and minimize inter-observer variability. We investigated the diagnostic utility of AI-based CCTA quantification (AI-QCT) of coronary atherosclerosis in coronary segments co-registered with intravascular ultrasound (IVUS) of diseased and non-diseased segments.</p> Materials and methods <p>Patients who underwent CCTA and IVUS in the INVICTUS registry (ClinicalTrials.gov: NCT04066062) were enrolled. Images were analyzed by independent core laboratories blinded to each modality’s findings. Vessel external elastic membrane (EEM), lumen, plaque volumes, plaque burden, and percent atheroma volume (PAV) were quantified in whole co-registered segments and subsegments containing non-calcified and low-attenuation plaques. A calcium index was calculated for the whole co-registered segment.</p> Results <p>A total of 108 vessels from 85 patients were included. Pearson’s correlation demonstrated strong associations between AI-QCT and IVUS in quantifying the EEM volume (r = 0.899), lumen volume (r = 0.943), and plaque volume (r = 0.833), length-normalized PAV (r = 0.851), and calcium index (r = 0.960) in the whole-segment analysis. Strong correlations were seen for vessel, lumen, and plaque volumes in non-calcified (Pearson’s coefficient: 0.95, 0.97, and 0.83, respectively) and low-attenuation (Pearson’s coefficient: 0.90, 0.86, and 0.86, respectively) plaque segments. The minimum lumen area was 0.61 ± 1.18 mm<sup>2</sup> (95% CI, −0.83 to −0.38) smaller by AI-QCT than IVUS, with a similar lumen area stenosis (mean difference, 1.26 ± 24.17; 95% CI, −3.37 to 5.90).</p> Conclusions <p>AI-QCT quantification of atherosclerosis burden showed high correlations and close agreement with IVUS in whole-segment and segments with non-calcified and low-attenuation plaques.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Coronary atheroma burden is a powerful predictor of cardiovascular events. Can AI-based coronary CT angiography (CCTA) accurately quantify atherosclerotic burden across the full disease spectrum&#xa0;when compared with&#xa0;intravascular ultrasound (IVUS)?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>AI-based CCTA quantification (AI-QCT) showed strong correlations with IVUS for plaque volume, burden, and calcium across whole coronary segments, including non-calcified and low-attenuation plaques.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>AI-QCT provides rapid, automatic, and accurate atherosclerosis quantification without reader-dependent variability, enabling standardized cardiovascular risk assessment, treatment monitoring, and therapeutic decision-making across all disease severity spectrum in routine clinical practice.</i></p> Graphical Abstract <p></p>

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Artificial intelligence-based coronary computed tomography angiography quantification of atherosclerosis burden: comparison with intravascular ultrasound in the INVICTUS Registry

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Rine Nakanishi,
  • Ryo Okubo,
  • Hitoshi Matsuo,
  • Yoshihiro Sobue,
  • Umihiko Kaneko,
  • Hideyuki Sato,
  • Shinichiro Fujimoto,
  • Yui Nozaki,
  • Takashi Kajiya,
  • Toru Miyoshi,
  • Keishi Ichikawa,
  • Mitsunori Abe,
  • Toshiro Kitagawa,
  • Hiroki Ikenaga,
  • Kazuhiro Osawa,
  • Mike Saji,
  • Nobuo Iguchi,
  • Gaku Nakazawa,
  • Kuniaki Takahashi,
  • Takeshi Ijichi,
  • Hiroshi Mikamo,
  • Akira Kurata,
  • Masao Moroi,
  • Raisuke Iijima,
  • Daniel Bandeira,
  • Abigail Demuyakor,
  • Helen Parise,
  • Shant Malkasian,
  • Gary S. Mintz,
  • Alexandra J. Lansky,
  • James P. Earls,
  • Daniel Chamié

摘要

Objectives

Automated artificial intelligence (AI)-based assessment of atherosclerosis burden applied to coronary computed tomography angiography (CCTA) can optimize image processing times, standardize interpretation, and minimize inter-observer variability. We investigated the diagnostic utility of AI-based CCTA quantification (AI-QCT) of coronary atherosclerosis in coronary segments co-registered with intravascular ultrasound (IVUS) of diseased and non-diseased segments.

Materials and methods

Patients who underwent CCTA and IVUS in the INVICTUS registry (ClinicalTrials.gov: NCT04066062) were enrolled. Images were analyzed by independent core laboratories blinded to each modality’s findings. Vessel external elastic membrane (EEM), lumen, plaque volumes, plaque burden, and percent atheroma volume (PAV) were quantified in whole co-registered segments and subsegments containing non-calcified and low-attenuation plaques. A calcium index was calculated for the whole co-registered segment.

Results

A total of 108 vessels from 85 patients were included. Pearson’s correlation demonstrated strong associations between AI-QCT and IVUS in quantifying the EEM volume (r = 0.899), lumen volume (r = 0.943), and plaque volume (r = 0.833), length-normalized PAV (r = 0.851), and calcium index (r = 0.960) in the whole-segment analysis. Strong correlations were seen for vessel, lumen, and plaque volumes in non-calcified (Pearson’s coefficient: 0.95, 0.97, and 0.83, respectively) and low-attenuation (Pearson’s coefficient: 0.90, 0.86, and 0.86, respectively) plaque segments. The minimum lumen area was 0.61 ± 1.18 mm2 (95% CI, −0.83 to −0.38) smaller by AI-QCT than IVUS, with a similar lumen area stenosis (mean difference, 1.26 ± 24.17; 95% CI, −3.37 to 5.90).

Conclusions

AI-QCT quantification of atherosclerosis burden showed high correlations and close agreement with IVUS in whole-segment and segments with non-calcified and low-attenuation plaques.

Key Points

Question Coronary atheroma burden is a powerful predictor of cardiovascular events. Can AI-based coronary CT angiography (CCTA) accurately quantify atherosclerotic burden across the full disease spectrum when compared with intravascular ultrasound (IVUS)?

Findings AI-based CCTA quantification (AI-QCT) showed strong correlations with IVUS for plaque volume, burden, and calcium across whole coronary segments, including non-calcified and low-attenuation plaques.

Clinical relevance AI-QCT provides rapid, automatic, and accurate atherosclerosis quantification without reader-dependent variability, enabling standardized cardiovascular risk assessment, treatment monitoring, and therapeutic decision-making across all disease severity spectrum in routine clinical practice.

Graphical Abstract