Background: Aortic dissection is a lift-threatening medical emergency associated with high rates of morbidity and mortality. The incidence rate of aortic dissection is estimated at 5 to 30 per 1 million people per year. The prompt and correct diagnosis of aortic dissection is critical. This study was to compare the ascending aortic image quality and the whole aortic radiation dose of high-pitch dual-source CT angiography and conventional dual-source CT angiography. Methods: A total of 110 consecutive patients with suspected aortic dissection and other aortic disorders were randomly divided into two groups. Group A underwent traditional scan mode and Group B underwent high-pitch dual-source CT scan mode. The image quality and radiation dose of two groups were compared. Results: Close interobserver agreement was found for image quality scores (κ = 0.87). The image quality of ascending aorta was significantly better in the high-pitch group than in the conventional group (2.78 ± 0.46 vs 1.57 ± 0.43, P < 0.001). There was no significant difference of the CT attenuation values, the aortic image noise and SNR between two groups. The mean radiation dose of high-pitch group was also significantly lower than that of conventional group (2.7 ± 0.6 mSv vs. 3.9 ± 0.9 mSv, P < 0.001). Conclusions: High-pitch dual-source CT angiography of the whole aorta can provide motion-artifact-free imaging of the ascending aorta at a low radiation dose compared to conventional protocol.

Objectives. This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods. This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), -nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results. Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions. Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Wen D, et al. Cardiovasc Imaging Asia. 2018 Jul;2(3):142-149. https://doi.org/10.22468/cvia.2018.00010