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Background Although the pathophysiology of white matter hyperintensities remains unclear, we can recently explore the possible relationship with white matter hyperintensities by using quantitative parameter. Purpose To demonstrate the relationship between bilateral distal internal carotid arterial tortuosity and total brain white matter hyperintensities volume in elderly individuals. Material and Methods A total of 345 patients (age > 65 years) with brain magnetic resonance (MR) examinations were retrospectively included (44.1% men; mean age = 72.1 ± 6.25 years; 55.9% ≥ 70 years). We measured the Tortuosity Index (TI) of the bilateral distal internal carotid artery and basilar artery on MR angiography imaging, and white matter hyperintensities volume on fluid-attenuated inversion recovery MR sequence. Multiple linear regression was used to assess the association of the TI with quantitatively derived brain white matter hyperintensity volume, after adjusting for demographics (age, sex), vascular risk factors (hypertension, diabetes, heart disease), and vessel diameters, total intracranial volume (TIV). Results Increased tortuosity of bilateral distal internal carotid artery was associated with greater burden of white matter hyperintensity volume (right: β = 11.223, P = 0.016; left: β = 20.701, P < 0.001). This relationship was independent of age and hypertension, both of which have been considered the strongest risk factors for white matter hyperintensities. Conclusion Our results suggest that tortuosity of the bilateral distal internal carotid artery is associated with white matter hyperintensities, independent of age and hypertension.

BackgroundThe purpose of this study was to evaluate the utility of multi-detector computed tomography (MDCT) angiography and transthoracic echocardiography (TTE) in the diagnosis of congenital coarctation of the aorta (CoA) and accompanying malformations in infants.Material/MethodsFrom January 2012 and December 2015, we enrolled 68 infants with clinically suspected CoA who underwent MDCT angiography and TTE in our hospital. Surgical correction was conducted to confirm the diagnostic accuracy of both examinations in all patients.ResultsIn this study, the diagnosis of CoA was confirmed infants by surgical results in 55 of 68 infants. The diagnostic accuracy, sensitivity, and specificity of MDCT angiography were 95.6%, 96.4%, and 92.3%, respectively. The diagnostic accuracy, sensitivity, and specificity of TTE were 88.2%, 90.9%, and 76.9%, respectively. There was no significant difference in diagnostic accuracy, sensitivity, and specificity between MDCT angiography and TTE (χ2=2.473, p>0.05, χ2=1.373, p>0.05 and χ2=1.182, p>0.05, respectively). In the diagnosis of concomitant cardiac abnormalities with CoA, the 2 methods also play different roles.ConclusionsMDCT angiography and TTE play different roles in the diagnosis of CoA and accompany malformations. MDCT angiography in the diagnosis of the extra-cardiac vascular malformations is better than TTE, and TTE is superior to MDCT angiography in diagnosing intracardiac malformation. Combined MDCT angiography and TTE is a relatively valuable, reliable, and noninvasive method in the diagnosis of CoA and accompany malformations in infants.

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Background. Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today’s clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network’s labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results. We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion. Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.