Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease

Abstract

Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

Keywords: Artificial intelligence; Atherosclerosis; Computed tomography; Plaque; Radiomics; Risk prediction.

Graphical Abstract

Figure 1

Artificial intelligence and machine learning. While AI describes a programme capable of performing tasks typical of human intelligence, machine learning refers to the process through which an AI system is trained to learn. The two main types of machine learning used in medicine are supervised and unsupervised learning. In the former, different algorithms such as regression or more advanced methods reflecting the structure of the human brain (neural networks), using decision trees or projecting a data set into a higher-order space to identify optimal separation planes (hyperplanes in support vector machines) or combination thereof are used to predict the class or value of a given label. In unsupervised learning (e.g. clustering), the data set is analysed to identify inherent patterns of the data, often using hierarchical or k-means clustering methods.

Figure 2

Radiomic characterization of textural features. For a given volume of interest, differences in the underlying histological structure will result in different texture patterns that can be described using higher-order features that reflect the unique spatial arrangement of voxels and their attenuation on computed tomography. Histogram-based first-order features only reflect the voxel attenuation distribution. Different texture patterns (same number of voxels with similar attenuation values but different location) may still have identical histogram and therefore similar first-order statistics.

Figure 3

Radiomic phenotyping of coronary lesions. Differences in coronary plaque composition will manifest as different radiomic texture patterns on computed tomography analysis, which can then be quantified using first- and higher-order radiomic features. Changes in these metrics can be used in an automated way to not only detect plaques but also produce a deep characterization of the histology and biology of a given lesion.

Figure 4

Radiomic phenotyping of perivascular fat to detect coronary inflammation. (A) Radiomic characterization of perivascular fat by means of the fat attenuation index to detect vascular effects on the adjacent fat. (B) Prognostic value of perivascular fat attenuation index phenotyping for all-cause and cardiac mortality in the Cardiovascular Risk Prediction using Computed Tomography study. Reproduced with permission from Oikonomou et al.

Figure 5

Radiomic phenotyping to detect biological hallmarks of dysfunctional adipose tissue. (A–C) Manhattan plots presenting the strength of association between adipose tissue radiomic features and the relative gene expression of TNFA (inflammation), COL1A1 (fibrosis), and CD31 (endothelial marker, vascularity). (D) Component plot of the three principal components of the adipose tissue radiome. (E) Comparison of nested linear regression models with relative gene expression as the dependent variable and (i) clinical risk factors alone (Model 1: age, sex, hypertension, hypercholesterolaemia, diabetes mellitus, and body mass index); (ii) Model 1 + mean attenuation (Model 2); and (iii) Model 2 + PVAT radiome (first three principal components) as the independent predictors. Imc, informational measure of correlation 2; L/H, low/high wavelet transformation; SALGLE, small area low grey-level emphasis; SDLGLE, small dependence low grey-level emphasis; SRLGLE, short-run low grey-level emphasis. Reproduced with permission from Oikonomou et al.

Figure 6

Computed tomography radiomics for precision medicine. A proposed workflow for the incorporation of machine learning-powered radiomic analysis of cardiac computed tomography scans in clinical practice. Radiomic analysis can reduce the analysis time and when integrated with electronic health records can provide automated recommendations to the physician regarding diagnosis and patient prognosis. At this stage, artificial and human intelligence can converge to enable the physician to select the optimal management plan based on all available data.