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Review
. 2023 Jan 13;13(2):307.
doi: 10.3390/diagnostics13020307.

Radiomics in Cardiac Computed Tomography

Isabelle Ayx  1 , Matthias F Froelich  1 , Stefan Baumann  2   3 , Theano Papavassiliu  2   3 , Stefan O Schoenberg  1
Affiliations
Review

Radiomics in Cardiac Computed Tomography

Isabelle Ayx et al. Diagnostics (Basel). .

Abstract

In recent years, there has been an increasing recognition of coronary computed tomographic angiography (CCTA) and gated non-contrast cardiac CT in the workup of coronary artery disease in patients with low and intermediate pretest probability, through the readjustment guidelines by medical societies. However, in routine clinical practice, these CT data sets are usually evaluated dominantly regarding relevant coronary artery stenosis and calcification. The implementation of radiomics analysis, which provides visually elusive quantitative information from digital images, has the potential to open a new era for cardiac CT that goes far beyond mere stenosis or calcification grade estimation. This review offers an overview of the results obtained from radiomics analyses in cardiac CT, including the evaluation of coronary plaques, pericoronary adipose tissue, and the myocardium itself. It also highlights the advantages and disadvantages of use in routine clinical practice.

Keywords: cardiac computed tomography; cardiac computed tomography angiography; cardiovascular disease; radiomics; texture analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of radiomics workflow. A high-quality CCTA image (A) is the foundation for radiomics application. After segmentation of the region of interest (plaques marked in yellow and orange (B), features can be extracted using dedicated software (parameters of various radiomics features (C). Features are selected in dependence on i.e., clinical parameters and models can be created further, for example by creating a heatmap (D).
Figure 2
Figure 2
Radiomics analysis of left ventricular myocardium. Segmentation of left ventricular myocardium in dedicated software (A). Feature extraction and connection of clinical parameter (Agatston Score), visualized in a Heatmap (B). Random Forest feature selection to identify differentiating features between different Agatston Scores (C). Selected differentiating feature visualized in boxplot in dependence of Agatston Score (D). Data derived from Ayx et al. [41].
See this image and copyright information in PMC

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