Supplemental material is available for this article.Multisystem inflammatory syndrome in children (MIS-C) is a newly defined condition associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The syndrome has been described as a "Kawasaki disease"-like illness and the spectrum of associated abnormalities, including vascular complications, remain to be fully defined. The novel findings of a large-vessel arteritis in this report will add to the understanding of this syndrome and its associated vascular complications. To evaluate type II endoleak nidus volume (ENV) in the arterial phase (ENV ) and delayed phase (ENV ) of the first postoperative CT angiography (CTA) as a predictor of persistent endoleak and aneurysm sac enlargement at follow-up CTA in patients with endovascular aneurysm repair (EVAR) of abdominal aortic aneurysm. Ninety-three patients (mean age ± standard deviation, 72 years ± 8; range, 56-88 years) with EVAR and type II endoleak were included in a single-institution retrospective study conducted between March 1, 2005, and December 31, 2018. ENV , ENV , change of volume (ENV ), and percentage of ENV and ENV in aneurysm sac volume (ASV) (ENV /ASV%, ENV /ASV%, respectively) were measured on first postoperative CTA images. The mean follow-up was 31.6 months ± 26.6 (range, 6-163.8 months). Patients were divided into two groups (group A, spontaneous resolution of endoleak without intervention [ = 29] and group B, persistent endoleak at follow-up CTA [ = 64]) and compared by using the Mann-Whitrecommended.© RSNA, 2021.ENVDP of the first postoperative CTA is an accurate predictor of persistent endoleak compared with ENVAP, and persistent endoleak is associated with aneurysm sac enlargement, in which earlier intervention is recommended.© RSNA, 2021.Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease. © RSNA, 2021. To determine the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the Radiological Society of North America (RSNA) categorizations in patients with clinically suspected coronavirus disease 2019 (COVID-19) infection. In this meta-analysis, studies from 2020, up to August 24, 2020 were assessed for inclusion criteria of studies that used CO-RADS or the RSNA categories for scoring chest CT in patients with suspected COVID-19. A total of 186 studies were identified. #link# After review of abstracts and text, a total of nine studies were included in this study. Patient information ( age, sex), CO-RADS and RSNA scoring categories, and other study characteristics were extracted. Study quality was assessed with the QUADAS-2 tool. Meta-analysis was performed with a random effects model. Nine studies (3283 patients) were included. Overall study quality was good, except for risk of non-performance of repeated reverse transcriptase polymerase chain reaction (RT-PCR) after negative initial Rhigher CORADS and RSNA classification categories.Coronary artery calcium (CAC) is a marker of overall coronary atherosclerotic burden in an individual. As such, it is an important tool in cardiovascular risk stratification and preventive treatment of asymptomatic patients with unclear cardiovascular disease risk. Several guidelines have recommended the use of CAC testing in shared decision making between the clinician and patient. With recent updates in clinical management guidelines and broad recommendations for CAC, there is a need for concise updated information on CAC interpretation on traditional electrocardiographically gated scans and nongated thoracic scans. Important points to report when interpreting CAC scans include the absolute Agatston score and the age, sex, and race-specific CAC percentile; general recommendations on time-to-rescan for individuals with a CAC score of 0; the number of vessels with CAC; the presence of CAC in the left main coronary artery; and specific highlighting of individuals with very high CAC scores of greater than 1000. When risk factor information is available, the 10-year coronary heart disease risk can also be easily assessed using the free online Multi-Ethnic Study of Atherosclerosis risk score calculator. Recent improvements in standardizing the reporting of CAC findings across gated and nongated studies, such as the CAC Data and Reporting System, show promise for improving the widespread clinical value of CAC in clinical practice. © RSNA, 2021.Cardiovascular computed tomography (CCT) is a well-validated noninvasive imaging tool with an ever-expanding array of applications beyond the assessment of coronary artery disease. These include the evaluation of structural heart diseases, congenital heart diseases, peri-procedural electrophysiology applications, and the functional evaluation of ischemia. This breadth requires a robust and diverse training curriculum to ensure graduates of CCT training programs meet minimum competency standards for independent CCT interpretation. This statement from the Society of Cardiovascular Computed Tomography aims to supplement existing societal training guidelines by providing a curriculum and competency framework to inform the development of a comprehensive, integrated training experience for cardiology and radiology trainees in CCT. Protein Tyrosine Kinase inhibitor is being published synchronously in Radiology Cardiothoracic Imaging, Journal of Cardiovascular Computed Tomography, and JACC Cardiovascular Imaging. © 2020 Society of Cardiovascular Computed Tomography.