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  • Author or Editor: Natasa Jávorszky x
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Abstract

Coronary computed tomography angiography has an emerging role in the diagnostic workup of coronary artery disease. Due to its high sensitivity and negative predictive value, coronary computed tomography angiography can rule out obstructive coronary artery diseases and substitute invasive coronary angiography in many cases. In addition, coronary computed tomography angiography provides a unique information beyond stenosis grading as it can visualize atherosclerosis and quantify its extent. Qualitative and quantitative plaque assessment provides an incremental value in the prediction of future major adverse cardiac events. Moreover, determining adverse plaque features has a potential to identify advanced atherosclerosis and patients at increased risk of acute coronary syndrome. Nevertheless, challenges may emerge with the process of quantifying coronary plaques due to limited reproducibility, lack of automated, standardized and validated techniques. Therefore, reliable quantified data are scarce due to the various computed tomography scanners and software platforms and investigations with small sample sizes. Radiomics and machine learning-based image processing methods are relatively new in the field of cardiovascular plaque imaging. These techniques hold the promise to improve diagnostic performance, reproducibility and prognostic value of computed tomography based plaque assessment.

Open access