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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.

AimsIdentification 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.Methods and resultsWe 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).ConclusionCoronary 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.

Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non-small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However , with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters. PLOS ONE | https://doi.org/10.1371/journal.pone.

he modified American Heart Association classification scheme categorizes coronary atherosclerotic plaques into six histologic classes: adaptive intimal thickening, pathologic intimal thickening, fibrous plaque, early fibroatheroma, late fibroatheroma, and thin-cap fibroatheroma (1,2). Adaptive and pathologic intimal thickening and fibrous plaque are considered early atherosclerotic lesions, whereas early and late fibroatheromas and thin-cap fibroatheroma are advanced atherosclerotic lesions associated with a higher risk of myocardial infarction (3-7). Therefore, the ability to noninvasively identify advanced lesions could improve risk stratification and prediction of future cardiovascular events. Coronary CT angiography can help characterize the morphologic characteristics and composition of plaques noninvasively. Noncalcified and partially calcified plaques

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In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.