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Background Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD. Methods We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors. Results In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129–0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM. Conclusions A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.

Background: Fractional flow reserve derived from computed tomography (CT-FFR) can be used to noninvasively evaluate the functions of coronary arteries and has been widely welcomed in the field of cardiovascular research. However, whether different image reconstruction schemes have an effect on CT-FFR analysis through single-and multiple-cardiac periodic images in the same patient has not been investigated.Methods: This study retrospectively enrolled 122 patients who underwent 320-row computed tomography (CT) examination with both single-and multiple-cardiac periodic reconstruction schemes; a total of 366 coronary arteries were analyzed. The lowest CT-FFR values of each vessel and the poststenosis CT-FFR values of the lesion-specific coronary artery were measured using the two reconstruction techniques.The Wilcoxon signed-rank test was used to compare differences in CT-FFR values between the two reconstruction techniques. Spearman correlation analysis was performed to determine the relationship between CT-FFR values derived using the two methods. Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to evaluate the consistency of CT-FFR values.Results: In all blood vessels, the lowest CT-FFR values showed no significant differences between the two reconstruction techniques in the left anterior descending artery (LAD; P=0.65), left circumflex artery (LCx; P=0.46), or right coronary artery (RCA; P=0.22). In blood vessels with atherosclerotic plaques, the poststenosis CT-FFR values (2 cm distal to the maximum stenosis) exhibited no significant differences between the two reconstruction techniques in the LAD (P=0.78), LCx (P=1.00), or RCA (P=1.00). The mean CT-FFR values of single-and multiple-cardiac periodic images showed excellent correlation and minimal bias in all groups.Conclusions: CT-FFR analysis based on an artificial intelligence deep learning neural network is stable and not affected by the type of 320-row CT reconstruction technology.

Background: Pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) is considered a marker of coronary inflammation. We aimed to explore the segments of PCAT that represent coronary inflammation in patients with acute coronary syndrome (ACS) and to identify patients with ACS and stable coronary artery disease (CAD) prior to intervention. Methods:We retrospectively enrolled consecutive patients with ACS and stable CAD who underwent invasive coronary angiography (ICA) after coronary computed tomography angiography (CCTA) from November 2020 to October 2021 at the Fourth Affiliated Hospital of Harbin Medical University. The fat attenuation index (FAI) was obtained using PCAT quantitative measurement software, and the coronary Gensini score was also calculated to indicate the severity of CAD. The differences and correlations between FAI within different radial distances of proximal coronary arteries were evaluated, and the recognition ability of FAI for patients with ACS and stable CAD was evaluated by establishing receiver operator characteristic (ROC) curves.Results: A total of 267 patients were included in the cross-sectional study, including 173 patients with ACS. With the increase of radial distance from the outer wall of proximal coronary vessels, the FAI decreased (P<0.001). The FAI around the proximal left anterior descending artery (LAD) within the reference diameter from the outer wall of the vessel (LAD_ref) had the highest correlation with the FAI around culprit lesions [r=0.587; 95% confidence interval (CI): 0.489-0.671; P<0.001]. The model based on clinical features, Gensini score, and LAD_ref had the highest recognition performance for patients with ACS and stable CAD [area under the curve (AUC): 0.663; 95% CI: 0.540-0.785].