A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Association of GSTM1 Null Allele with Prostate Cancer Risk: Evidence from 36 Case-Control Studies

WOS:000312385200061Peer reviewe

A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients

sensor

Enhanced efficacy with reduced toxicity of chemotherapy drug 5-fluorouracil by synergistic treatment with Abnormal Savda Munziq from Uyghur medicine

Abstract Background Abnormal Savda Munziq (ASMq) is a traditional prescription in Uyghur Medicine, and its treatment of complex diseases such as tumors and asthma has been proven to be effective in Uyghur medical clinical practice. The efficacy-enhancing and toxicity-reducing properties of ASMq were studied on mice with transplanted cervical cancer (U27) tumors, which were treated with 5-fluorouracil (5-FU) in this work. Methods To investigate the synergistic effect of ASMq and 5-FU on U27 cells, inhibitory effects on cell proliferation were determined through a MTT assay. 48 Kunming mice which were randomly divided in to 6 groups: control group, model group, 5-FU group, 5-FU combine with ASMq low-dose group, 5-FU combine with ASMq medium-dose group, and 5-FU combine with ASMq high- dose group, the inhibition rate of the tumor, the viscera indexes, and the content of serum tumor necrosis factor-α (TNF-α), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined. The expression levels of transforming growth factor-β1 (TGF-β1) and human papillomavirus type 16 E2 (HPV16 E2) protein were assessed by Western blot. Pathological changes in the liver were observed. Result The inhibition rates of tumors, the 5-FU + ASMq.H group(80.64%), 5-FU + ASMq.M group (90.67%), 5-FU + ASMq.L group (72.03%) and 5-FU group (66.89%), clearly indicated that the effects of tumor inhibition. The thymus index and spleen index were increased, and the serum concentration of TNF-α increased while ALT and AST concentrations were decreased, and TNF-α protein expression were increased while TGF-β1 and HPV16 E2 were decreased. ASMq might can improve livers central vein hyperemia and interstitial edema, and preserve the radial structure of the hepatic cords. Conclusions The results suggested that ASMq might reduce toxicity and enhance the efficacy of the chemotherapeutic drug 5-fluorouracil in the treatment of cervical carcinoma

Design and synthesis of 6-substituted amino-4-oxa-1-azabicyclo[3,2,0]heptan-7-one derivatives as cysteine protease inhibitors

A series of 6-substituted amino-4-oxa-1-azabicyclo[3,2,0]heptan-7-one compounds was designed and synthesized as a new class of inhibitors for cysteine proteases cathepsins B, L, K, and S. One compound (5S,6S)-6-(N-benzyloxycarbonyl-l-phenylalanyl) amino-4-oxa-1-azabicyclo[3,2,0]heptan-7-one showed excellent cathepsin L and K inhibition activity with IC50 at a low nanomolar range.NRC publication: Ye