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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Aim

The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).

Methods

A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.

Results

In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT ( = 0.007) and plaque volume ( = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 7.05, 95%CI: 1.44-34.63, p = 0.016) and plaque volume (HR: 3.84, 95%CI: 1.44-10.27, p = 0.007) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.150).

Conclusions

Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CT-FFR in patients with PCI.

© 2025 The Author(s).
© 2025 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-09-03

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/content/journals/cmir/10.2174/0115734056335065250426150739
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Prognostic Value Of Deep Learning Based RCA PCAT and Plaque Volume Beyond CT-FFR In Patients With Stent Implantation
Curr. Med. Imaging 21, E15734056335065 (2025); https://doi.org/10.2174/0115734056335065250426150739
/content/journals/cmir/10.2174/0115734056335065250426150739
/content/journals/cmir/10.2174/0115734056335065250426150739
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  • Article Type:     Research Article
Keyword(s): Artificial intelligence; Coronary artery disease; Coronary computed tomography angiography; Myocardial infraction; Percutaneous coronary intervention; Pericoronary adipose tissue attenuation computed tomography; Prognosis

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