Search

It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.

This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.

Both Dyna CT,a rotational faultage reconstructed technique,and digital subtraction angiography(DSA)play important roles in the diagnosis and treatment of arteriovenous malformations(AVM).Three-dimensional Dyna CT can provide the spatial information of AVM nidus,while two-dimensional DSA can provide the time information for distinguishing arteries and veins.To illustrate the location relationship of the nidus,arteries and veins at the same time,these two imaging modalities need to be fused.In this paper,a two-dimensional to three-dimensional back projection and growing method is proposed,which realizes the image fusion of two-dimensional DSA and threedimensional Dyna CT and achieves the differentiation of arteries and veins in Dyna CT.The experimental results showed that the fusion image could present both the position information of AVM nidus and the dynamic information of the blood vessels.Therefore,the proposed method can help surgeons locate the AVM abnormality and make operation plan more accurately.

Objective: To determine the effects of estrogen deficiency on the proliferation and osteogenic differentiation of mandibular bone marrow stromal cells (mBMSC)., Methods: Ten 8-week-old female SD rats were randomly divided into two groups, ovariectomized group (OVX, n = 5) and sham-operation group (n = 5). All rats were anesthetized and both ovaries of OVX-rats were gently removed. Sham-operation rats were treated with the same incisions to expose the ovaries but without any hurt to them. One month after the operation, the mandibular bones were gently separated and mBMSC were isolated. Methyl thiazolyl tetrazolium (MTT) assay, alkaline phosphotase (ALP) activity, alizarin red staining, real-time reverse transcription (RT)-PCR and Western blotting were respectively used to examine the proliferative activity and osteogenic potential of mBMSC., Results: MTT results showed that OVX-mBMSC exhibited the decreased proliferative activity as compared with Sham-mBMSC. ALP activity of OVX-mBMSC [(0.710±0.011) Sigma unit/protein] was lower than that of Sham-mBMSC [(1.512±0.021) Sigma unit/protein] (P < 0.01). Alizarin red staining showed that OVX-mBMSC formed less calcified nodules than Sham-mBMSC. Ca(2+) concentration analysis showed Ca(2+) of OVX-mBMSC [(0.433±0.045) µg/g] was less than Sham-mBMSC [(1.453±0.131) µg/g] (P < 0.01). Real-time RT-PCR and Western blotting results showed that the expression of osteogenic markers (Alp, Runx2/RUNX2, Osx/OSX, Ocn/OCN) in OVX-mBMSC was significantly inhibited as compared with Sham-mBMSC (P < 0.05)., Conclusions: Estrogen deficiency significantly inhibits the proliferation and osteogenic capacity of mBMSC.

Objective: The aim of this study was to examine the levels of prostaglandin E2 in periapical exudates before and after root canal treatment., Methods: Periapical exudates specimens were quantitatively sampled from root canals of 25 teeth at consecutive treatment visits, and the concentration of prostaglandin E2 in each sample was determined using a commercial kit., Results: The mean prostaglandin E2 levels significantly decreased after the endodontic therapy. Significant correlations between the levels of prostaglandin E2 and the clinical features of periapical periodontitis were also found in this study., Conclusion: The results suggest that the level of prostaglandin E2 in periapical exudates reflects the state of periapical periodontitis.