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. 2023 Dec:386:117363.
doi: 10.1016/j.atherosclerosis.2023.117363. Epub 2023 Oct 29.

Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference

Hiroyuki Omori  1 , Hitoshi Matsuo  1 , Shinichiro Fujimoto  2 , Yoshihiro Sobue  1 , Yui Nozaki  2 , Gaku Nakazawa  3 , Kuniaki Takahashi  3 , Kazuhiro Osawa  4 , Ryo Okubo  5 , Umihiko Kaneko  6 , Hideyuki Sato  7 , Takashi Kajiya  8 , Toru Miyoshi  9 , Keishi Ichikawa  9 , Mitsunori Abe  10 , Toshiro Kitagawa  11 , Hiroki Ikenaga  11 , Mike Saji  12 , Nobuo Iguchi  13 , Takeshi Ijichi  14 , Hiroshi Mikamo  15 , Akira Kurata  16 , Masao Moroi  17 , Raisuke Iijima  17 , Shant Malkasian  18 , Tami Crabtree  19 , James K Min  19 , James P Earls  20 , Rine Nakanishi  21
Affiliations
Free article

Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference

Hiroyuki Omori et al. Atherosclerosis. 2023 Dec.
Free article

Abstract

Background and aims: Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).

Methods: The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS.

Results: This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively.

Conclusions: AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.

Keywords: Artificial intelligence; Computed tomography; Coronary artery disease; Lipid-rich plaque; Near-infrared spectroscopy.

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