Coronary artery disease (CAD) is a common condition caused by the accumulation of atherosclerotic plaques. It can be classified into stable CAD or acute coronary syndrome. Coronary computed tomography angiography (CCTA) has a high negative predictive value and is used as the first examination for diagnosing stable CAD, particularly in patients at intermediate-to-high risk. CCTA is also adopted for diagnosing acute coronary syndrome, particularly in patients at low-to-intermediate risk. Myocardial ischemia does not always co-exist with coronary artery stenosis, and the positive predictive value of CCTA for myocardial ischemia is limited. However, CCTA has overcome this limitation with recent technological advancements such as CT perfusion and CT-fractional flow reserve. In addition, CCTA can be used to assess coronary artery plaques. Thus, the indications for CCTA have expanded, leading to an increased demand for radiologists. The CAD reporting and data system (CAD-RADS) 2.0 was recently proposed for standardizing CCTA reporting. This RADS evaluates and categorizes patients based on coronary artery stenosis and the overall amount of coronary artery plaque and links this to patient management. In this review, we aimed to review the major trials and guidelines for CCTA to understand its clinical role. Furthermore, we aimed to introduce the CAD-RADS 2.0 including the assessment of coronary artery stenosis, plaque, and other key findings, and highlight the steps for CCTA reporting. Finally, we aimed to present recent research trends including the perivascular fat attenuation index, artificial intelligence, and the advancements in CT technology.

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale-for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed.

The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients.
Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort.
Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001).
The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

The aim of this work is to review Coronary Artery Disease Imaging Reporting and Data System (CAD-RADS) that was designed to standardize reporting language and improve the communication of data among radiologists and clinicians. Stenotic lesions are graded into 5 grades ranging from 0 (no stenosis) to 5 (total occlusion), where the highest grade represents the final score. The expert consensus platform has added 4 special modifiers (non-diagnostic, stent, graft, and vulnerability) to aid patient management through linking these scores with decision algorithm and treatment plan. Adherence to standard imaging protocol; knowledge of normal, variant, and anomalous anatomy; and skillful evaluation of stenosis are important for proper utilization of this reporting system. Lastly, radiologists should be aware of the inherited benefits, limitations, and common pitfalls of this classification system.