Background—: Napkin-ring sign (NRS) is an independent prognostic imaging marker of major adverse cardiac events. However, identification of NRS is challenging because of its qualitative nature. Radiomics is the process of extracting thousands of quantitative parameters from medical images to create big-data data sets that can identify distinct patterns in radiological images. Therefore, we sought to determine whether radiomic analysis improves the identification of NRS plaques. Methods and Results—: From 2674 patients referred to coronary computed tomographic angiography caused by stable chest pain, expert readers identified 30 patients with NRS plaques and matched these with 30 non-NRS plaques with similar degree of calcification, luminal obstruction, localization, and imaging parameters. All plaques were segmented manually, and image data information was analyzed using Radiomics Image Analysis package for the presence of 8 conventional and 4440 radiomic parameters. We used the permutation test of symmetry to assess differences between NRS and non-NRS plaques, whereas we calculated receiver-operating characteristics’ area under the curve values to evaluate diagnostic accuracy. Bonferroni-corrected P 0.80. Short- and long-run low gray-level emphasis and surface ratio of high attenuation voxels to total surface had the highest area under the curve values (0.918; 0.894 and 0.890, respectively). Conclusions—: A large number of radiomic features are different between NRS and non-NRS plaques and exhibit excellent discriminatory value.

Radiologic images are vast three-dimensional data sets in which each voxel of the underlying volume represents distinct physical measurements of a tissue-dependent characteristic. Advances in technology allow radiologists to image pathologies with unforeseen detail, thereby further increasing the amount of information to be processed. Even though the imaging modalities have advanced greatly, our interpretation of the images has remained essentially unchanged for decades. We have arrived in the era of precision medicine where even slight differences in disease manifestation are seen as potential target points for new intervention strategies. There is a pressing need to improve and expand the interpretation of radiologic images if we wish to keep up with the progress in other diagnostic areas. Radiomics is the process of extracting numerous quantitative features from a given region of interest to create large data sets in which each abnormality is described by hundreds of parameters. From these parameters datamining is used to explore and establish new, meaningful correlations between the variables and the clinical data. Predictive models can be built on the basis of the results, which may broaden our knowledge of diseases and assist clinical decision making. Radiomics is a complex subject that involves the interaction of different disciplines; our objective is to explain commonly used radiomic techniques and review current applications in cardiac computed tomography imaging.

Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability by a single, widely available non-invasive technique may provide the opportunity to identify vulnerable plaques and vulnerable patients in broad populations. Our aim was to assess whether radiomic analysis outperforms conventional assessment of coronary computed tomography angiography (CTA) images to identify invasive and radionuclide imaging markers of plaque vulnerability. We prospectively included patients who underwent coronary CTA, sodium-fluoride positron emission tomography (NaF¹⁸-PET), intravascular ultrasound (IVUS), and optical coherence tomography (OCT). We assessed seven conventional plaque features and calculated 935 radiomic parameters from CTA images. In total, 44 plaques of 25 patients were analysed. The best radiomic parameters significantly outperformed the best conventional CT parameters to identify attenuated plaque by IVUS [fractal box counting dimension of high attenuation voxels vs. non-calcified plaque volume, area under the curve (AUC): 0.72, confidence interval (CI): 0.65–0.78 vs. 0.59, CI: 0.57–0.62; P < 0.001], thin-cap fibroatheroma by OCT (fractal box counting dimension of high attenuation voxels vs. presence of low attenuation voxels, AUC: 0.80, CI: 0.72–0.88 vs. 0.66, CI: 0.58–0.73; P < 0.001), and NaF¹⁸-positivity (surface of high attenuation voxels vs. presence of two high-risk features, AUC: 0.87, CI: 0.82–0.91 vs. 0.65, CI: 0.64–0.66; P < 0.001). Coronary CTA radiomics identified invasive and radionuclide imaging markers of plaque vulnerability with good to excellent diagnostic accuracy, significantly outperforming conventional quantitative and qualitative high-risk plaque features. Coronary CTA radiomics may provide a more accurate tool to identify vulnerable plaques compared with conventional methods. Further larger population studies are warranted.

CT based technologies have evolved considerably in recent years. Coronary CT angiography (CTA) provides robust assessment of coronary artery disease (CAD). Early coronary CTA imaging—as a gate-keeper of invasive angiography—has focused on the presence of obstructive stenosis. Coronary CTA is currently the only non-invasive imaging modality for the evaluation of non-obstructive CAD, which has been shown to contribute to adverse cardiac events. Importantly, improved spatial resolution of CT scanners and novel image reconstruction algorithms enable the quantification and characterization of atherosclerotic plaques. State-of-the-art CT imaging can therefore reliably assess the extent of CAD and differentiate between various plaque features. Recent studies have demonstrated the incremental prognostic value of adverse plaque features over luminal stenosis. Comprehensive coronary plaque assessment holds potential to significantly improve individual risk assessment incorporating adverse plaque characteristics, the extent and severity of atherosclerotic plaque burden. As a result, several coronary CTA based composite risk scores have been proposed recently to determine patients at high risk for adverse events. Coronary CTA became a promising modality for the evaluation of functional significance of coronary lesions using CT derived fractional flow reserve (FFR-CT) and/or rest/dynamic myocardial CT perfusion. This could lead to substantial reduction in unnecessary invasive catheterization procedures and provide information on ischemic burden of CAD. Discordance between the degree of stenosis and ischemia has been recognized in clinical landmark trials using invasive FFR. Both lesion stenosis and composition are possibly related to myocardial ischemia. The evaluation of lesion-specific ischemia using combined functional and morphological plaque information could ultimately improve the diagnostic performance of CTA and thus patient care. In this review we aimed to summarize current evidence on comprehensive coronary artery plaque assessment using coronary CTA.

Radiomics-based machine learning of coronary CT angiography in the identification of advanced atherosclerotic lesions has higher diagnostic accuracy compared with visual and histogram-based assessment. Background Visual and histogram-based assessments of coronary CT angiography have limited accuracy in the identification of advanced lesions. Radiomics-based machine learning (ML) could provide a m...