Coronary Artery Disease Reporting And Data System Research Articles

Intraindividual Comparison of Ultrahigh-Spatial-Resolution Photon-Counting Detector CT and Energy-Integrating Detector CT for Coronary Stenosis Measurement.

A recent simulation study proposed that stenosis measurements on coronary computed tomography (CT) angiography are influenced by the improved spatial resolution of photon-counting detector (PCD)-CT. The aim of the current study was to evaluate the impact of ultrahigh-spatial-resolution (UHR) on coronary stenosis measurements and Coronary Artery Disease Reporting and Data System (CAD-RADS) reclassification rates in patients undergoing coronary CT angiography on both PCD-CT and energy-integrating detector (EID)-CT and to compare measurements against quantitative coronary angiography. Patients with coronary calcification on EID-CT (collimation, 192×0.6 mm) were prospectively enrolled for a research coronary CT angiography with UHR PCD-CT (collimation, 120×0.2 mm) within 30 days (between April 1, 2023 and January 31, 2024). PCD-CT was acquired with the same or lower CT dose index and equivalent contrast media volume as EID-CT. Percentage diameter stenosis (PDS) for calcified, partially calcified, and noncalcified lesions were compared between scanners. Patient-level reclassification rates for CAD-RADS were evaluated. The accuracy of PDS measurements was validated against quantitative coronary angiography in patients who underwent invasive coronary angiography. In total, PDS of 278 plaques were quantified in 49 patients (calcified, 202; partially calcified, 51; noncalcified, 25). PCD-CT-based PDS values were lower than EID-CT measurements for calcified (45.1±20.7 versus 54.6±19.2%; P<0.001) and partially calcified plaques (44.3±19.6 versus 54.9±20.0%; P<0.001), without significant differences for noncalcified lesions (39.1±15.2 versus 39.0±16.0%; P=0.98). The reduction in stenosis degrees led to a 49.0% (24/49) reclassification rate to a lower CAD-RADS with PCD-CT. In a subset of 12 patients with 56 lesions, UHR-based PDS values showed higher agreement with quantitative coronary angiography (mean difference, 7.3%; limits of agreement, -10.7%/25.2%) than EID-CT measurements (mean difference, 17.4%; limits of agreement, -6.9%/41.7%). Compared with conventional EID-CT, UHR PCD-CT results in lower PDS values and more accurate stenosis measurements in coronary plaques with calcified components and leads to a substantial Coronary Artery Disease Reporting and Data System reclassification rate in 49.0% of patients.

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

Computed tomography angiography and coronary artery disease–reporting and data system and a 5-year prognostic major adverse cardiovascular and cerebral event outcome study in a symptomatic Southeast Asian population

Background This study investigated the relationship between coronary artery disease–reporting and data system (CAD-RADS) for coronary computed tomography angiogram (CCTA) and major adverse cardiovascular and cerebral event (MACE) in a symptomatic Southeast Asian, Thai population over a 5-year period. Methods A retrospective cohort study of Thai patients without known CAD who underwent CCTA for CAD symptoms. CCTA images and 5-year health data were reviewed for CAD-RADS and MACE. MACE consists of all-cause mortality, cardiovascular death, acute coronary syndrome, heart failure hospitalization, and stroke. Results In total 336 patients were evaluated. The median follow-up period was 6.4 years. The overall MACE incidence was 63 cases (18.8%). The MACE event rate was progressively increased with higher CAD-RADS categories; CAD-RADS 3 [hazard ratio (HR), 3.62; P = 0.015], CAD-RADS 4a (HR, 3.50; P = 0.024), CAD-RADS 4b &amp; 5 (HR, 7.56; P = 0.001). The risk of MACE increased significantly in the moderate to severe CAD burden group (HR, 5.58; P = 0.01). Kaplan–Meier curve showed a significant rise in MACE with higher CAD-RADS categories (P = 0.01). Conclusion CAD-RADS classification has a significant prognostic value in Southeast Asian, Thai population with cardiac symptoms.

The Plaque Analysis Classifies the Coronary Artery Disease-Reporting and Data System (CAD-RADS) Stenosis and Plaque Burden Categories: Association of the Plaque Features, Fat Attenuation Index, Coronary Computed Tomography Fractional Flow Reserve, and the Combination of Stenosis and Calcification.

The coronary artery disease-reporting and data system (CAD-RADS) 2.0 is used to standardize the reporting of coronary computed tomography angiography (CCTA) results. Artificial intelligence software can quantify the plaque composition, fat attenuation index, and fractional flow reserve. To analyze plaque features of varying severity in patients with a combination of CAD-RADS stenosis and plaque burden categorization and establish a random forest classification model. The data of 100 patients treated between April 2021 and February 2022 were retrospectively collected. The most severe plaque observed in each patient was the target lesion. Patients were categorized into three groups according to CAD-RADS: CAD-RADS 1-2 + P0-2, CAD-RADS 3-4B + P0-2, and CAD-RADS 3-4B + P3-4. Differences and correlations between variables were assessed between groups. AUC, accuracy, precision, recall, and F1 score were used to evaluate the diagnostic performance. A total of 100 patients and 178 arteries were included. The differences of computed tomography fractional flow reserve (CT-FFR) (H = 23.921, p < 0.001),the volume of lipid component (H = 12.996, p = 0.002),the volume of fibro-lipid component (H = 8.692, p = 0.013),the proportion of lipid component volume (H = 22.038, p < 0.001),the proportion of fibro-lipid component volume (H = 11.731, p = 0.003),the proportion of calcification component volume (H = 11.049, p = 0.004), and plaque type (χ2 = 18.110, p = 0.001) was statistically significant. CT-FFR, volume and proportion of lipid and fibro-lipid components of plaques, the proportion of calcified components, and plaque type were valuable for CAD-RADS stenosis + plaque burden classification, especially CT-FFR, volume, and proportion of lipid and fibro-lipid components. The model built using the random forest was better than the clinical model (AUC: 0.874 vs. 0.647).

Association between severity of nonalcoholic fatty liver disease and major adverse cardiovascular events in patients assessed by coronary computed tomography angiography

BackgroundThe effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear.MethodsA total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan–Meier analysis were used to assess poor prognosis.ResultsDuring the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54–5.66]; p = 0.001) and CAD-RADS categories 3–5 (HR = 16.31[6.34–41.92]; p < 0.001) were independent risk factors for MACEs. The Kaplan–Meier analysis showed that moderate to severe NAFLD patients had a worsen prognosis than mild NAFLD patients (log-rank p < 0.001). Moreover, the combined receiver operating characteristic curve of the severity of NAFLD and CAD-RADS category showed a good predicting performance for the risk of MACEs, with an area under the curve of 0.849 (95% CI = 0.786–0.911).ConclusionThe severity of NAFLD was independent risk factor for MACEs in patients with obstructive CAD, having CAD-RADS 3–5 categories on CCTA.

Prognostic significance of CAD-RADS for patients with suspected coronary artery disease: A systematic review and meta-analysis

Abstract Background Coronary Artery Disease-Reporting and Data System (CAD-RADS) is a standardized reporting system that offers a structured method for interpreting and reporting results obtained through coronary computed tomography angiography. It has been deemed useful in the prognostication of patients with suspected coronary artery disease (CAD). Purpose The present meta-analysis sought to assess the prognostic value of CAD-RADS in individuals with suspected CAD. Materials and Methods We conducted a systematic search of the electronic databases of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE. A random-effects model was implemented to evaluate the pooled hazard ratio (HR) for each CAD-RADS category and area under the receiver operating characteristics curve for predicting major adverse cardiovascular events. Results Data from 37 596 coronary computed tomography angiography examinations from 13 studies were analyzed. During follow-up, 2,536 (6.7%) adverse events were observed. Pooled HRs for prediction of adverse events were significant for all CAD-RADS categories, with incremental increase in HRs with progressively higher categories. For prediction of all-cause mortality, all categories except CAD-RADS 1 showed significant HR compared with CAD-RADS 0. Combination of CAD-RADS to conventional clinical risk factors and CAC resulted in a high predictive capacity for adverse events (pooled area under the receiver operating characteristics curve: 0.82 ([95% confidence interval, 0.73-0.91]). Conclusion The CAD-RADS category imparts information on the CAD severity and shows incremental increase in HR for adverse events with progressively higher categories, especially beyond CAD-RADS4b.

Diagnostic performance of 3.0 T unenhanced Dixon water-fat separation coronary MR angiography in patients with low-to-intermediate risk of coronary artery disease

PurposeTo evaluate the diagnostic performance of 3.0 T unenhanced compressed-sensing sensitivity encoding (CS-SENSE) Dixon water-fat separation coronary MR angiography (CMRA) in patients with low-to-intermediate risk of coronary artery disease (CAD) and its ability to grade the severity of CAD based on Coronary Artery Disease Reporting and Data System (CAD-RADS). MethodsA total of 55 patients who was clinically evaluated as low-to-intermediate risk of CAD were finally included to undergo both 3.0 T CS-SENSE water-fat separation CMRA and coronary computed tomography angiography (CCTA), and 11 of them also underwent X-ray coronary angiography (CAG). The severity of coronary artery disease was graded in patients who had completed both CCTA and CMRA examinations by the use of CAD-RADS reports for the patients with stable chest pain, and the diagnostic consistency between the two approaches was evaluated. Diagnostic performance of CMRA was assessed using the combination of CCTA and CAG as the reference standard for excluding or confirming CAD respectively. ResultsThe sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 3.0 T unenhanced water-fat separation coronary MRA were 90.0%, 95.0%, 81.8%, 97.4% and 94.0% for a patient-based analysis respectively. In comparison with CCTA, 3.0 T Dixon water-fat separation CMRA demonstrated excellent consistency in grading the severity of coronary heart disease according to CAD-RADS (0.77 for kappa value). ConclusionIn the group of low-to-intermediate probability for CAD, 3.0 T unenhanced CS-SENSE Dixon water-fat separation CMRA can present satisfactory diagnostic performance for the exclusion of CAD with high sensitivity and negative predictive value as well as the evaluation of grading the severity of coronary artery disease.

Ultrahigh-Spatial-Resolution Photon-counting Detector CT Angiography of Coronary Artery Disease for Stenosis Assessment.

Background Coronary CT angiography is a first-line test in coronary artery disease but is limited by severe calcifications. Photon-counting-detector (PCD) CT improves spatial resolution. Purpose To investigate the effect of improved spatial resolution on coronary stenosis assessment and reclassification. Materials and Methods Coronary stenoses were evaluated prospectively in a vessel phantom (in vitro) containing two stenoses (25%, 50%), and retrospectively in patients (in vivo) who underwent ultrahigh-spatial-resolution cardiac PCD CT (from July 2022 to April 2023). Images were reconstructed at standard resolution (section thickness, 0.6 mm; increment, 0.4 mm; Bv44 kernel), high spatial resolution (section thickness, 0.4 mm; increment, 0.2 mm; Bv44 kernel), and ultrahigh spatial resolution (section thickness, 0.2; increment, 0.1 mm; Bv64 kernel). Percentages of diameter stenosis (DS) were compared between reconstructions. In vitro values were compared with the manufacturer specifications of the phantom and patient results were assessed regarding effects on Coronary Artery Disease Reporting and Data System (CAD-RADS) reclassification. Results The in vivo sample included 114 patients (mean age, 68 years ± 9 [SD]; 71 male patients). In vitro percentage DS measurements were more accurate with increasing spatial resolution for both 25% and 50% stenoses (mean bias for standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 10.1%, 8.0%, and 2.3%; P < .001). In vivo results confirmed decreasing median percentage DS with increasing spatial resolution for calcified stenoses (n = 161) (standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 41.5% [IQR, 27.3%-58.2%], 34.8% [IQR, 23.7%-55.1%], and 26.7% [IQR, 18.6%-44.3%]; P < .001), whereas noncalcified (n = 13) and mixed plaques (n = 19) did not show evidence of a difference (P ≥ .88). Ultrahigh-spatial-resolution reconstructions led to reclassification of 62 of 114 (54.4%) patients to lower CAD-RADS category than that assigned using standard resolution. Conclusion In vivo and in vitro coronary stenosis assessment improved for calcified stenoses by using ultrahigh-spatial-resolution PCD CT reconstructions, leading to lower percentage DS compared with standard resolution and clinically relevant rates of reclassification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by McCollough in this issue.

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.

The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

Prognostic value of mitral annular calcification in coronary atherosclerotic disease assessed by coronary computed tomographic angiography

Abstract Background: There is a lack of evidence on the link between mitral annular calcification (MAC) and coronary atherosclerotic diseases. The present investigation was undertaken to detect the clinical and prognostic value of MAC in coronary atherosclerotic diseases in patients who underwent coronary computed tomographic (CT) angiography. Materials and Methods: Two hundred and five individuals with MAC and without it (n = 85 and 120, respectively) were included in the present cross-sectional study. Coronary artery disease-reporting and data system (CAD-RADS) at coronary CT angiography was used to define the severity of coronary atherosclerotic diseases. Patients were classified into no or non-significant CAD (CAD-RADS 0–2) and significant CAD (CAD-RADS 3–5) according to the severity of coronary atherosclerotic diseases. The association of MAC with two mentioned groups (no or non-significant CAD and significant CAD) was assessed using the Chi-squared test and logistic regression in crude and adjusted models. Results: Patients with MAC were significantly older (69.34 ± 8.20 vs. 60.64 ± 11.42, P &lt; 0.001), had lower glomerular infiltration rate (69.67 ± 20.92 vs. 78.00 ± 20.23, P = 0.005), and higher coronary artery calcification score (352.87 ± 495.85 vs. 200.55 ± 426.13, P = 0.05) in comparison to those without MAC. However, the significant difference between the two groups regarding coronary artery calcification score disappeared after adjustment for confounders (P = 0.14). In addition, a statistically significant positive link between MAC and significant CAD was observed (odds ratio [OR] [95% confidence interval (CI)]: 1.96 [1.04–3.71], P = 0.04). Nevertheless, the association became statistically insignificant after adjustment for confounders (OR [95% CI]: 1.60 [0.78–3.28], P = 0.2). Conclusion: The findings of the study revealed that MAC has no independent prognostic value in coronary atherosclerotic diseases evaluated by coronary CT angiography.

The prevalence of coronary artery disease in patients presenting with 'non-anginal chest pain'.

The National Institute for Health and Care Excellence (NICE) advise against routine testing for coronary artery disease (CAD) in patients with non-anginal chest pain (NACP). This clinical audit sought to establish the prevalence of significant CAD in this cohort using computed tomography angiography (CTCA) and evaluate differences in the prevalence of cardiovascular risk factors between those with and without obstructive coronary disease. Over 23 months, 866 patients with NACP underwent CTCA. Patients were separated into three groups for analysis depending on the degree of CAD on CTCA using the CAD-RADS (Coronary Artery Disease Reporting and Data System) scoring system; no evidence of CAD (group 1), a degree of CAD requiring medical therapy only (group 2), significant CAD defined as a CAD-RADS score 4A/B or 5 (group 3). Cardiovascular risk factors were compared between the groups. We found 11.5% had significant CAD (group 3), 58.3% required medical therapy (group 2) and 30.1% had no CAD (group 1). There were 32 patients who required coronary revascularisation. Patients in group 2 and 3 were more likely to be male (p<0.001) and older (p<0.001) when compared to patients in group 1. Patients in group 3 were more likely to be hypertensive (p=0.008) and have higher Qrisk2 scores (p<0.001) when compared with those in group 1. In conclusion, NICE guidelines for NACP may result in a significant proportion of patients with CAD being underdiagnosed, including some with severe disease requiring revascularisation. This analysis suggests age, male gender, Qrisk2 score and hypertension are predictors of CAD in this cohort.

Epicardial fat volume and its association with cardiac arrhythmias in CT coronary angiography.

This retrospective study aimed to investigate the epicardial fat volume in cardiac computed tomography (CT), its relationship with cardiac arrhythmias, and its correlation with the coronary artery disease reporting and data system (CAD-RADS) score. Ninety-six patients who underwent CT coronary angiography (CTCA) were included in this study. Patient data, including demographic information, clinical history, and imaging data were collected retrospectively. Epicardial fat volume was quantified using a standardised algorithm, the CAD-RADS scoring system was applied to assess the extent of coronary artery disease (CAD). Descriptive statistics, correlation analyses, and receiver operating characteristics methods were used. The study found a significant correlation between epicardial fat volume and CAD-RADS score (r2 = 0.31; p < 0.001), indicating the known influence of epicardial fat on CAD risk. Moreover, patients with higher epicardial fat volumes were more likely to experience cardiac tachyarrhythmia (p < 0.001). Receiver operating characteristic analysis established a threshold value of 123 cm3 for epicardial fat volume to predict tachyarrhythmia with 80% sensitivity (AUC = 0.69). In this study a volume of at least 123 cm3 epicardial fat in native coronary calcium scans is associated with cardiac tachyarrhythmia. In these patients, careful selection of suitable imaging protocols is advised.

Intra-individual comparison of coronary artery stenosis measurements between energy-integrating detector CT and photon-counting detector CT

AbstractPurposeTo compare intra-individual percentage diameter stenosis (PDS) measurements of coronary artery stenoses between energy-integrating detector computed tomography (EID-CT) and a clinical photon-counting detector computed tomography (PCD-CT) systems using similar acquisition and reconstruction settings.MethodsPatients (n = 23, mean age of 65 ± 12.1 years, out of these 16 (69.6%) male) were imaged on a conventional EID- and a clinical PCD-CT system with a median of 5.5 (3.0–12.5) days apart. Sequential CCTA scans were acquired and reconstructed using similar settings, including a vascular Bv36 kernel, a tube voltage of 110 kVp for EID-CT vs 120 kVp for PCD-CT, a slice thickness of 0.5 for EID-CT vs 0.6 for PCD-CT, and an iterative reconstruction strength of 3 on EID-CT vs a virtual monoenergetic reconstruction at 55 keV and quantum iterative reconstruction level of 3 on PCD-CT. Radiation dose, contrast volume, and injection parameters were matched as similarly as possible between the systems. PDS measurements were performed according to the coronary artery disease reporting and data system (CAD-RADS) by two trained readers and compared between the different modalities using the Wilcoxon rank sum test, Spearman correlation, and Bland-Altman analysis.ResultsPCD-CT measured significantly lower PDS values than EID-CT [PDSEID-CT: 45.1% (35.1%–64.0%) vs. PDSPCD-CT 44.2% (32.4%–61.0%), P &lt; 0.0001]. This difference led to a mean bias of 1.8 (LoA −3.0/6.5) with an excellent ICC (0.99) value among EID- and PCD-CT. The mean intra-individual deviation between the examinations was 1.8% between the scanners. This led to CAD-RADS re-classification in 3/23 cases (13.0%, new-lower class) for the first reader, and in 4/23 cases (13.0%, new-lower and 4.4%, new-higher class) for the second reader. Inter-reader agreement between the two readers for each stenosis was very strong (ICC = 0.98).ConclusionsCoronary artery stenosis measurements from PCD-CT correlate strongly to EID-CT-based measurements, despite the tendency of the measurement from PCD-CT being lower. This difference led to a change in CAD-RADS classification in 17.4% of patients. The effects on clinical decision-making, downstream testing, and prognosis have to be evaluated in future studies.

The differences between patients with nonalcoholic fatty liver disease (NAFLD) and those without NAFLD, as well as predictors of functional coronary artery ischemia in patients with NAFLD.

Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease associated with metabolic syndrome. It is the most common cause of cryptogenic cirrhosis. The disease is also involved in the occurrence and development of type 2 diabetes and atherosclerosis and can directly affect the outcome of patients with coronary heart disease. Therefore, the focus of treatment of nonalcoholic fatty liver disease has also begun to focus on the treatment of risk factors for atherosclerotic heart disease. In this study, we investigated the difference between patients with coronary artery stenosis combined with NAFLD and those without NAFLD and evaluated the predictive factors and value of functional coronary artery ischemia in patients with NAFLD. Many clinical factors (such as age, BMI, hyperglycemia) and imaging parameters (such as CACS grade) in the NAFLD group were different from those in the non-NAFLD group. The predictive model combined with multiple influencing factors has a good value in predicting coronary artery ischemia in patients with NAFLD. We collected the clinical and imaging data of patients who underwent coronary computed tomography angiographyand coronary artery calcification score (CACS) scans between January and June 2023. A total of 392 patients were included and divided into the NAFLD group and the non-NAFLD group. Based on CT fractional flow reserve (CT-FFR), patients with NAFLD were divided into CT-FFR ≤ 0.08 group and CT-FFR > 0.08 group. Significant differences were observed between the non-NAFLD and NAFLD groups in terms of age, body mass index, hyperglycemia, hyperlipidemia, triglyceride, high-density lipoprotein, coronary artery disease-reporting and data system (CAD-RADS) classification, CACS classification, number of diseased coronary arteries, and CT-FFR ≤ 0.80 ratio (p < .05). The CAD-RADS and CACS classifications can independently predict functional coronary artery ischemia in NAFLD patients. The combined use of CAD-RADS and CACS classifications resulted in an area under the curve of 0.819 (95% confidence interval: 0.761-0.876) for predicting coronary artery ischemia in NAFLD patients, which was higher than the individual classification methods (CAD-RADS: 0.762, CACS: 0.742) (p = .000). There are differences between patients with coronary artery stenosis and NAFLD and those without NAFLD. The CAD-RADS classification and CACS classification can economically and efficiently predict functional coronary artery ischemia in patients with NAFLD, which has crucial value in clinical diagnosis and treatment.

Quantifying left ventricular myocardial strain in patients with different CAD-RADS levels based on computed tomography feature tracking technology

To evaluate myocardial strain in patients with different coronary artery disease-reporting and data system (CAD-RADS) levels using the computed tomography (CT) feature tracking technology and to investigate the relationship of myocardial strain with coronary artery calcium scores (CACs) and the degree of coronary artery stenosis. We prospectively enrolled 237 consecutive patients to undergo coronary CT angiography. The participants were divided into the following groups: control (n = 87), CAD-RADS 1 (n = 43), CAD-RADS 2 (n = 43), CAD-RADS 3 (n = 38), and CAD-RADS 4 and above (n = 26). Myocardial strains were analyzed by commercial software, and CACs and coronary stenosis were assessed on post-processing stations. Differences between multiple groups were analyzed using one-way analysis of variance or the Kruskal–Wallis test. Logistic regression were used to analyze the effects of dichotomous variables. As the CAD-RADS level increased, the global circumferential strain (GCS), global longitudinal strain (GLS) and global radial strain (GRS) of the left ventricle based on CT gradually decreased. A significant correlation was observed between global myocardial strain and CACs (GRS: r = − 0.219, GCS: r = 0.189, GLS: r = 0.491; P < 0.05). The independent predictors of obstructive CAD were age (β = 0.065, odds ratio [OR] = 1.067, P = 0.005), left ventricular ejection fraction (β = 0.145, OR = 1.156, P = 0.047), and GLS (β = 0.232, OR = 1.261, P = 0.01). CT-derived GLS of the left ventricle is correlated with CAD-RADS levels and CACs. It may be a better indicator than CACs to reflect the severity of CAD.

Coronary artery disease reporting and data system (CAD-RADS), vascular inflammation and plaque vulnerability

BackgroundCoronary artery disease reporting and data system (CAD-RADS) predicts future cardiovascular events in patients with coronary artery disease (CAD). However, information on vascular inflammation and vulnerability remains scarce. MethodsPatients who underwent coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) prior to coronary intervention were enrolled. All three coronary arteries were evaluated for CAD-RADS score and pericoronary adipose tissue (PCAT) attenuation, while the culprit vessel was analyzed for plaque vulnerability by OCT. ResultsA total of 385 patients with 915 lesions were divided into two groups based on CAD-RADS score: 103 (26.8%) were categorized as CAD-RADS 4b/5 and 282 (73.2%) as CAD-RADS ≤4a. Patients with CAD-RADS 4b/5 had a higher level of PCAT attenuation (mean of 3 coronary arteries) than those with CAD-RADS ≤4a (−68.4 ​± ​6.7 HU vs. −70.1 ​± ​6.5, P ​= ​0.022). The prevalence of macrophage was higher, and lipid index was greater in patients with CAD-RADS 4b/5 than CAD-RADS ≤4a (94.2% vs. 83.0%, P ​= ​0.004, 1845 vs. 1477; P ​= ​0.003). These associations were significant in the culprit vessels of patients with chronic coronary syndrome but not in those with acute coronary syndromes. ConclusionsHigher CAD-RADS score was associated with higher levels of vascular inflammation and plaque vulnerability.

Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study.

Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC). For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06. The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.

Comparison between the Computed Tomography (CT) Coronary Angiography by Using “Coronary Artery Disease - Reporting and Data System (CAD-RADS)” and the Conventional Coronary Angiography in Assessment of the Coronary Artery Stenosis Severity

Background Coronary CTA is established as the first-line diagnostic imaging tool to exclude coronary artery disease (CAD) at low to intermediate risk. Reporting and interpretation of the findings gap between the referring physicians and radiologist are wide. Aim of the Work To evaluate the clinical efficacy of Computed Tomography (CT) coronary angiography by using Coronary Artery Disease - Reporting and Data System (CAD-RADS) versus invasive coronary angiography in the assessment of coronary artery stenosis severity. Patients and Methods The study included 24 patients suffered from typical chest pain or fatigue, 9 were female patients and 15 were male patients, with mean patient ages above 40 years old. The study was conducted at Ain Shams University Hospitals. Results The main goals of the CAD-RADS are to decrease variability between readers and enhance communication between interpreting and referring doctors, which affects the best patient care outcome. Many clinical case scenarios are not accurately addressed by CAD-RADS and in which classes of CAD-RADS category is not straightforward. Multiple scenarios are still vague and at the maximum point of conflict about the adoption of CAD-RADS role in practice. Conclusion Hoping the next new versions of CAD-RADS classification would have addressed those vague situations and scenarios.

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study.

This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p<0.001), and high-density lipoprotein (HDL-C) lower (p<0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.

Atherosclerosis on CT coronary angiography and the risk of long-term cardiovascular events after liver transplantation.

Computed tomography coronary angiography (CTCA) is increasingly utilized for preoperative risk stratification before liver transplantation (LT). We sought to assess the predictors of advanced atherosclerosis on CTCA using the recently developed Coronary Artery Disease-Reporting and Data System (CAD-RADS) score and its impact on the prediction of long-term major adverse cardiovascular events (MACE) following LT. We conducted a retrospective cohort study of consecutive patients who underwent CTCA for LT work-up between 2011 and 2018. Advanced atherosclerosis was defined as coronary artery calcium scores > 400 or CAD-RADS score ≥ 3 (≥50% coronary artery stenosis). MACE was defined as myocardial infarction, heart failure, stroke, or resuscitated cardiac arrest. Overall, 229 patients underwent CTCA (mean age 66 ± 5y, 82% male). Of these, 157 (68.5%) proceeded with LT. The leading etiology of cirrhosis was hepatitis (47%), and 53% of patients had diabetes before transplant. On adjusted analysis, male sex (OR 4.6, 95% CI 1.5-13.8, p = 0.006), diabetes (OR 2.2, 95% CI 1.2-4.2, p = 0.01) and dyslipidemia (OR 3.1, 95% CI 1.3-6.9, p = 0.005) were predictors of advanced atherosclerosis on CTCA. Thirty-two patients (20%) experienced MACE. At a median follow-up of 4 years, CAD-RADS ≥ 3, but not coronary artery calcium scores, was associated with a heightened risk of MACE (HR 5.8, 95% CI 1.6-20.6, p = 0.006). Based on CTCA results, 71 patients (31%) commenced statin therapy which was associated with a lower risk of all-cause mortality (HR 0.48, 95% CI 0.24-0.97, p = 0.04). The standardized CAD-RADS classification on CTCA predicted the occurrence of cardiovascular outcomes following LT, with a potential to increase the utilization of preventive cardiovascular therapies.