A Comparative Study of Clinical and Aortic Morphological Characteristics between Bovine Aortic Arch and Normal Aortic Arch in Patients with Acute Type B Aortic Dissection.

INTRODUCTION: The purpose of this study was to analyze the difference in clinical and aortic morphological features between the bovine aortic arch and normal aortic arch in patients with acute type B aortic dissection (aTBAD). METHODS: A total of 133 patients diagnosed with aTBAD were retrospectively collected. Based on aortic arch morphology, they were divided into the bovine aortic arch group (n = 20) and the normal aortic arch group (n = 113). Aortic morphological features were assessed on computed tomographic angiography. Clinical and aortic morphological features were then compared between the bovine aortic arch and normal aortic arch groups. RESULTS: Patients in the bovine aortic arch group were significantly younger and with higher weight and BMI than the normal aortic arch group (p < 0.001, p = 0.045, and p = 0.016, respectively). The total aortic length in the bovine aortic arch group was significantly shorter than that in the normal aortic arch group (p = 0.039). The tortuosity of descending thoracic aorta, the tortuosity of descending aorta, and the angulation of aortic arch were significantly lower in the bovine aortic arch group (p = 0.004, p = 0.015, and p = 0.023, respectively). The width of descending aorta, the height of aorta arch, and the angle of ascending aorta were significantly smaller in the bovine aortic arch group (p = 0.045, p = 0.044, and p = 0.042, respectively). CONCLUSION: When the aTBAD occurred, patients with bovine aortic arch were prone to be younger and with higher BMI than those with normal aortic arch. The aortic curvature and the total aortic length were lower in patients with bovine aortic arch.

The role of epicardial and pericoronary adipose tissue radiomics in identifying patients with non-ST-segment elevation myocardial infarction from unstable angina.

Objectives: This study aimed to ascertain if the radiomics features of epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could identify non-ST-segment elevation myocardial infarction (NSTEMI) from unstable angina (UA). Materials and methods: This retrospective case-control study included 108 patients with NSTEMI and 108 controls with UA. All patients were separated into training cohort (n = 116), internal validation cohort 1 (n = 50), and internal validation cohort 2 (n = 50) based on the time order of admission. The internal validation cohort 1 used the same scanner and scan parameters as the training cohort, while the internal validation cohort 2 used different canners and scan parameters than the training cohort. The EAT and PCAT radiomics features selected by maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted to build logistic regression models. Finally, we developed an EAT radiomics model, three vessel-based (right coronary artery [RCA], left anterior descending artery [LAD], and left circumflex artery [LCX]) PCAT radiomics models, and a combined model by combining the three PCAT radiomics models. Discrimination, calibration, and clinical application were employed to assess the performance of all models. Results: Eight radiomics features of EAT, sixteen of RCA-PCAT, fifteen of LAD-PCAT, and eighteen of LCX-PCAT were selected and used to construct radiomics models. The area under the curves (AUCs) of the EAT, RCA-PCAT, LAD-PCAT, LCX-PCAT and the combined models were 0.708 (95% CI: 0.614-0.802), 0.833 (95% CI:0.759-0.906), 0.720 (95% CI:0.628-0.813), 0.713 (95% CI:0.619-0.807), 0.889 (95% CI:0.832-0.946) in the training cohort, 0.693 (95% CI:0.546-0.840), 0.837 (95% CI: 0.729-0.945), 0.766 (95% CI: 0.625-0.907), 0.675 (95% CI: 0.521-0.829), 0.898 (95% CI: 0.802-0.993) in the internal validation cohort 1, and 0.691 (0.535-0.847), 0.822 (0.701-0.944), 0.760 (0.621-0.899), 0.674 (0.517-0.830), 0.866 (0.769-0.963) in the internal validation cohort 2, respectively. Conclusion: Compared with the RCA-PCAT radiomics model, the EAT radiomics model had a limited ability to discriminate between NSTEMI and UA. The combination of the three vessel-based PCAT radiomics may have the potential to distinguish between NSTEMI and UA.

Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques.

PURPOSE: To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability. METHODS: We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses. RESULTS: Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001-1.116), plaque ulceration (OR 6.106; 95% CI 1.933-19.287), and carotid rim sign (OR 3.285; 95% CI 1.203-8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful. CONCLUSION: Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.