Cardiac computed tomography radiomics: a narrative review of current status and future directions

Abstract

Background and objective: In an era of profound growth of medical data and rapid development of advanced imaging modalities, precision medicine increasingly requires further expansion of what can be interpreted from medical images. However, the current interpretation of cardiac computed tomography (CT) images mainly depends on subjective and qualitative analysis. Radiomics uses advanced image analysis to extract numerous quantitative features from digital images that are unrecognizable to the naked eye. Visualization of these features can reveal underlying connections between image phenotyping and biological characteristics and support clinical outcomes. Although research into radiomics on cardiovascular disease began only recently, several studies have indicated its potential clinical value in assessing future cardiac risk and guiding prevention and management strategies. Our review aimed to summarize the current applications of cardiac CT radiomics in the cardiovascular field and discuss its advantages, challenges, and future directions.

Methods: We searched for English-language articles published between January 2010 and August 2021 in the databases of PubMed, Embase, and Google Scholar. The keywords used in the search included computed tomography or CT, radiomics, cardiovascular or cardiac.

Key content and findings: The current applications of radiomics in cardiac CT were found to mainly involve research into coronary plaques, perivascular adipose tissue (PVAT), myocardial tissue, and intracardiac lesions. Related findings on cardiac CT radiomics suggested the technique can assist the identification of vulnerable plaques or patients, improve cardiac risk prediction and stratification, discriminate myocardial pathology and etiologies behind intracardiac lesions, and offer new perspective and development prospects to personalized cardiovascular medicine.

Conclusions: Cardiac CT radiomics can gather additional disease-related information at a microstructural level and establish a link between imaging phenotyping and tissue pathology or biology alone. Therefore, cardiac CT radiomics has significant clinical implications, including a contribution to clinical decision-making. Along with advancements in cardiac CT imaging, cardiac CT radiomics is expected to provide more precise phenotyping of cardiovascular disease for patients and doctors, which can improve diagnostic, prognostic, and therapeutic decision making in the future.

Keywords: Cardiac computed tomography; cardiovascular disease; phenotyping; radiomics.

Figure 1

A common workflow of radiomics. Radiomics workflow is commonly composed of image acquisition and preprocessing, image segmentation, feature extraction and selection, model building and validation. The yellow arrow and frame refer to the coronary plaques in the axial and multiplanar reconstruction images based on CCTA, respectively.

Figure 2

The current applications of cardiac CT radiomics. The current applications of radiomics on cardiac CT mainly focus on coronary plaques, pericoronary adipose tissue, myocardial tissue, and intracardiac lesions. CT, computed tomography. The white arrow above refers to the lesion of left atrium. The white arrow below refers to the coronary plaque.

Figure 3

Representative examples of plaques with and without the NRS. Volume-rendered and cross-sectional images of plaques with NRS in the top (A, C, and E) and their matched plaques in the bottom (B, D, and E) are shown. Green dashed lines indicate the location of cross-sectional planes. Colors indicate different computed tomographic attenuation values [reproduced with permission from reference (18)]. NCP, non-calcified plaque; NRS, napkin-ring sign.

Figure 4

Radiomic phenotyping of coronary perivascular adipose tissue. The perivascular adipose tissue of the right and left coronary arteries (left main and proximal of left anterior descending artery) is used to segment and calculate radiomic statistics (A). A correlation plot with hierarchical clustering of 1,391 stable radiomic features in 1575 SCOT-HEART patients, revealing distinct clusters of radiomic variance (B). Heat map of scaled radiomic features in the SCOT-HEART population revealing between-patient variance across the cohort (C) [reproduced with permission from reference (23)]. LCA, left coronary artery; PVAT, perivascular adipose tissue; RCA, right coronary artery; SCOT-HEART, the Scottish computed tomography of the heart.