Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study

Abstract

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.

The Predictive Value of Lesion-Specific Fat Attenuation Index for Major Adverse Cardiovascular Events in Patients with Type 2 Diabetes

“…Previous studies have established that RCA-FAI was markedly elevated in patients with T2DM as opposed to those without, and this holds true irrespective of the degree of arterial stenosis or the vulnerability of plaque formations [6] . Further investigation focusing on individuals with T2DM has highlighted that the effectiveness of RCA-FAI in identifying coronary artery disease was more representative than the Left anterior descending (LAD) and Left circum ex (LCX) arteries, making it a better diagnostic tool for coronary artery disease in T2DM patients [20] . Additionally, RCA-FAI has been identi ed as an independent prognostic factor for the onset of microvascular complications in T2DM patients [21] .…”

Relationship between different clinical characteristics and pericoronary adipose tissue attenuation values quantified from coronary computed tomographic angiography (CCTA) in patients without coronary heart disease (CHD)

“…Among the studies conducted on the relationship between PCAT and the clinic, most studies focused on the correlation between PCAT and ACS or CHD ( 2 , 21 , 22 ). Clinical risk factors combined with CT-based imaging parameters and PCAT radiomic features in patients with type 2 diabetes showed that this model effectively diagnosed CHD in patients with type 2 diabetes ( 21 ). A cross-sectional study showed that the mean PCAT attenuation values were correlated with plaque parameters and the degree of stenosis in patients with ACS ( 22 ).…”

The predictive value of lesion-specific pericoronary fat attenuation index for major adverse cardiovascular events in patients with type 2 diabetes

“…Previous studies have established that RCA-FAI was markedly elevated in patients with T2DM as opposed to those without, and this holds true irrespective of the degree of arterial stenosis or the vulnerability of plaque formations [ 6 ]. Further investigation focusing on individuals with T2DM has highlighted that the effectiveness of RCA-FAI in identifying coronary artery disease was more representative than the LAD and LCX arteries, making it a better diagnostic tool for coronary artery disease in patients with T2DM [ 23 ]. Additionally, RCA-FAI has been identified as an independent prognostic factor for the onset of microvascular complications in patients with T2DM [ 24 ].…”