Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis

Eur J Nucl Med Mol Imaging. 2024 Oct 15. doi: 10.1007/s00259-024-06948-8. Online ahead of print.

Abstract

Purpose: This study aimed to test whether the coronary artery calcium (CAC) burden on attenuation correction computed tomography (CTac), measured using artificial intelligence (AI-CACac), correlates with coronary flow capacity (CFC) and prognosis.

Materials and methods: We retrospectively enrolled patients who underwent [13N]ammonia positron emission tomography (PET) between September 2021 and May 2023. CTac data were obtained from all the patients. Patients with (history of) acute coronary syndrome, previous coronary stent insertion or bypass surgery, or left ventricular ejection fraction < 40% were excluded. The total Agatston score measured using a dedicated AI-CAC quantification software on CTac was defined as AI-CACac. The correlations between AI-CACac and PET-measured myocardial blood flow (MBF) and CFC and significant ischaemia (summed difference score ≥ 7) were analysed. Their prognostic values for major cardiovascular events (MACE), including death, nonfatal myocardial infarction, hospitalisation due to angina pectoris or heart failure, and late (> 90 days) revascularisation, were also evaluated.

Results: In total, 289 patients were included in this study. Significant negative correlations were found between AI-CACac and stress MBF (ρ = -0.363, p < 0.001) and MFR (ρ = -0.305, p < 0.001). AI-CACac > 10 was associated with a significantly higher prevalence of impaired CFC (31% vs. 7%, p < 0.001) and significant ischaemia (20% vs. 7%), which remained significant after adjusting for other risk factors. MACE occurred in 49 (17%) patients (median follow-up, 284 days), and those who experienced MACE had significantly higher AI-CACac (median, 166 vs. 56; p = 0.039). However, multivariable analysis revealed an independent prognostic association among impaired CFC, diabetes, smoking, but not for AI-CACac.

Conclusion: AI-measured CACac correlates well with PET-measured MBF and CFC, but its prognostic significance requires further validation.

Keywords: Artificial intelligence; Attenuation correction computed tomography; Coronary calcium; Coronary flow capacity; Positron emission tomography; Prognosis.