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:The aim of the study is to investigate the performance of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) in the same patient evaluated by different systolic and diastolic scans, aiming to explore whether 320-slice CT scanning acquisition protocol has an impact on CT-FFR value.Methods: One hundred forty-six patients with suspected coronary artery stenosis who underwent CCTA examination were included into the study. The prospective electrocardiogram gated trigger sequence scan was performed and electrocardiogram editors selected 2 optimal phases of systolic phase (preset collection trigger at 25% of R-R interval) and diastolic phase (preset collection trigger at 75% of R-R interval) for reconstruction. The lowest CT-FFR value (the CT-FFR value at the distal end of each vessel) and the lesion CT-FFR value (at 2 cm distal to the stenosis) after coronary artery stenosis were calculated for each vessel. The difference of CT-FFR values between the 2 scanning techniques was compared using paired Wilcoxon signed-rank test. Pearson correlation value and Bland-Altman were performed to evaluate the consistency of CT-FFR values.Results: A total of 366 coronary arteries from the remaining 122 patients were analyzed. There was no significant difference regarding the lowest CT-FFR values between systole phase and diastole phase across all vessels. In addition, there was no significant difference in the lesion CT-FFR value after coronary artery stenosis between systole phase and diastole phase across all vessels. The CT-FFR value between the 2 reconstruction techniques had excellent correlation and minimal bias in all groups. The correlation coefficient of the lesion CT-FFR values for left anterior descending branch, left circumflex branch, and right coronary artery were 0.86, 0.84, and 0.76, respectively.Conclusions: Coronary computed tomography angiography-derived fractional flow reserve based on artificial intelligence deep learning neural network has stable performance, is not affected by the acquisition phase technology of 320-slice CT scan, and has high consistency with the evaluation of hemodynamics after coronary artery stenosis.
Purpose This study aimed to evaluate the clinical value of the fractional flow reserve derived from coronary computed tomography angiography (CT-FFR) and fat attenuation index (FAI) in predicting coronary revascularization. Methods Patients with known or suspected CAD who underwent coronary computed tomography angiography (CCTA) and subsequent invasive coronary angiography were screened. All CCTA data were calculated by a cloud workstation in standard Digital Imaging and Communications in Medicine format. Lesion-specific CT-FFR, distal-tip CT-FFR, and FAI were analyzed by core laboratories blinded to patient management. Results A total of 94 patients who received CCTA followed by invasive coronary angiography were identified and analyzed; 282 vessels were included for analysis. Overall, 54 (57.4%) patients with 72(25.5%) vessels demonstrated revascularization. In the multivariate model, FAI (odds ratio [OR]: 1.19; p < 0.001), lesion-specific CT-FFR (OR: 3.80; p = 0.009), and distal-tip CT-FFR (OR: 4.20; p = 0.008) values were identified as independent negative predictors. All receiver operating characteristic curves were above the reference line. The areas under the receiver operating characteristic curve for lesion-specific CT-FFR, distal-tip CT-FFR, and FAI were 0.798, 0.767, and 0.802, respectively. When the optimal threshold value of FAI was − 86 HU, the sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy for predicting revascularization were 88.9%, 59.0%, 42.7%, 93.2%, and 0.66, respectively. The corresponding values for the lesion-specific CT-FFR were 73.6%, 81.0%, 56.3%, 88.2%, and 0.78, respectively. Conclusions In patients with documented CAD on CCTA, adjunctive noninvasive functional testing based on the CT-FFR and FAI yielded similar overall accuracy for prediction of coronary revascularization. However, a significant difference was observed in diagnostic sensitivity of the FAI; the lesion-specific CT-FFR demonstrated the highest specificity. In conclusion, CT-FFR and FAI derived from quantitative CCTA improved the prediction of future revascularization. These parameters can potentially identify patients likely to require revascularization on referral for cardiac catheterization.
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