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

Hiroyuki Omori, Hitoshi Matsuo, Shinichiro Fujimoto, Yoshihiro Sobue, Yui Nozaki, Gaku Nakazawa, Kuniaki Takahashi, Kazuhiro Osawa, Ryo Okubo, Umihiko Kaneko, Hideyuki Sato, Takashi Kajiya, Toru Miyoshi, Keishi Ichikawa, Mitsunori Abe, Toshiro Kitagawa, Hiroki Ikenaga, Mike Saji, Nobuo Iguchi, Takeshi IjichiHiroshi Mikamo, Akira Kurata, Masao Moroi, Raisuke Iijima, Shant Malkasian, Tami Crabtree, James K. Min, James P. Earls, Rine Nakanishi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number117363
JournalAtherosclerosis
Volume386
DOIs
Publication statusPublished - 12-2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

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