The value of color doppler ultrasonography combined with computed tomography angiography and magnetic resonance angiography in the preoperative quantification and classification of carotid body tumors: a retrospective analysis.

BACKGROUND: Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) provide accurate vascular imaging information, but their use may be contraindicated. Color Doppler ultrasonography (CDU) provides simple, safe, noninvasive, and reproducible imaging. We therefore investigated the role of preoperative CDU combined with CTA and MRA in the quantification, typing, and diagnosis of carotid body tumors (CBTs). METHODS: We retrospectively analyzed patients with CBTs categorized into group A (type I [n = 1] and type II [n = 10]) or group B (type III [n = 56]) per the intraoperative Shamblin classification. CDU, CTA, and MRA characteristics of CBTs were observed, surgical results were correlated, and the diagnostic threshold of the CBT classification was calculated. RESULTS: CBTs were usually located at the common carotid artery bifurcation, encircling the carotid artery. An increased angle was found between the internal and external carotid arteries. On CDU, CBTs primarily presented as homogeneous hypoechoic masses with clear boundaries, rich flow signals, and a high-speed, low-resistance artery-like flow spectrum. CTA showed uniform or heterogeneous marked enhancement. MRA showed mixed T1 and slightly longer T2 signals and uniform or uneven obvious enhancement. With increases in the lesion size, amount of blood transfused, and operation time, the intraoperative classification level and possibility of skull-base invasion increased. When the maximum diameter of the lesion, the volume of the tumor, the distance between the upper margin of the tumor to the mastoid and the mandibular angle were 3.10 cm, 10.15 cm3, - 3.26 cm, and 0.57 cm, respectively, the largest Youden index was the best diagnostic boundary value for Shamblin type III tumors. CONCLUSIONS: CDU combined with CTA and MRA can accurately evaluate the size and classification of CBTs.

Elastic Net based sparse feature learning and classification for Alzheimer's disease identification.

Magnetic resonance imaging (MRI) has been successfully applied to investigate neuron pathological changes. Since the high dimension of observation data, sparse feature learning plays an important role in overcoming the challenge of high variable dimension and low sample size problem among the disease identification. In this paper, sparse Elastic Net (EN) was used to extract low dimension features and to identify the Alzheimer's disease (AD). Compare with principal component analysis (PCA) method, the EN method can solve the problems of less samples and high correlations between variables. For those variables sharing the same biological phenomenon, it selected whole groups into the model automatically once one variable among them was selected. Unlike other subspace learning methods, the proposed method used less man-made feature setting. The problems of dimension reduction and classification were conducted into a similar formulation. Experimental results illustrated the effectiveness of the proposed method.

Compartmental sparse feature selection method for Alzheimer's disease identification.

For high-dimensional magnetic resonance imaging (MRI) data, many feature selection methods have been proposed to reduce feature dimension in the study of computer-aided Alzheimer's disease (AD) diagnosis. This paper presents a compartmental sparse feature selection method used for AD identification. Based on the derived atlas-based regions-of-interest (ROIs) of brain, the proposed method partitioned the T1-weighted MRI data into several compartments. It performs feature selection and classification compartmentally according to the local feature dimension estimation and local feature selection using sparse principal component analysis (SPCA) method followed with elastic-net logistic regression (ENLR) classifier. Experimental results showed that the proposed method improves the classification performance for small ROIs with high computational efficiency.