Skip to main content

ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1462566

Identification of Patients with Unstable Angina Based on Coronary CT Angiography: The Application of Pericoronary Adipose Tissue Radiomics

Provisionally accepted
Weisheng Zhan Weisheng Zhan 1Yixin Li Yixin Li 1Hui Luo Hui Luo 2Jiang He Jiang He 1Jiao Long Jiao Long 1Yang Xu Yang Xu 2Ying Yang Ying Yang 1*
  • 1 Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China
  • 2 Nanchong Central Hospital, Nanchong, Sichuan Province, China

The final, formatted version of the article will be published soon.

    Objective: To explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).In this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina (n=120) were well-matched with those having stable angina (n=120). All patients were randomly divided into training (70%) and testing (30%) datasets. Automatic segmentation was performed on the pericoronary adipose tissue surrounding the proximal segments of the left anterior descending artery (LAD), left circumflex coronary artery (LCX), and right coronary artery (RCA). Corresponding radiomic features were extracted and selected, and the fat attenuation index (FAI) for these three vessels was quantified.Machine learning techniques were employed to construct the FAI and radiomic models.Multivariate logistic regression analysis was used to identify the most relevant clinical features, which were then combined with radiomic features to create clinical and integrated models. The performance of different models was compared in terms of area under the curve (AUC), calibration, clinical utility, and sensitivity.

    Keywords: Pericoronary adipose tissue, Radiomics, Coronary computed tomography angiography, coronary heart disease, machine learning

    Received: 26 Jul 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Zhan, Li, Luo, He, Long, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ying Yang, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.