Purpose To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade one, and 2) propose a radiomics-based strategy for cancer grading using texture features. Materials and Methods 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T MR. Mann-Whitney U test was applied to select features with significant difference between low-and high-grade groups (p<0.05). Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading. Results A total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant inter-group difference (p<0.05). By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.861, 82.9%, 78.4% and 87.1%, respectively. Conclusion Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively.

3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.

BackgroundPrevious studies had showed that Apelin 13 could protect against apoptosis induced by ischemic/reperfusion (I/R). However, the mechanisms whereby Apelin 13 protected brain I/R remained to be elucidated. The present study was designed to determine whether Apelin 13 provided protection through AMPK/GSK-3β/Nrf2 pathway.MethodsIn vivo, the I/R model was induced and Apelin 13 was given intracerebroventricularly 15 min before reperfusion. The neurobehavioral scores, infarction volumes, and some cytokines in the brain were measured. For in vitro study, PC12 cells were used. To clarify the mechanisms, proteases inhibitors or siRNA were used. Protein levels were investigated by western blotting.ResultsThe results showed that Apelin 13 treatment significantly reduced infarct size, improved neurological outcomes, decreased brain edema, and inhibited cell apoptosis, oxidative stress, and neuroinflammation after I/R. Apelin 13 significantly increased the expression of Nrf2 and the phosphorylation levels of AMPK and GSK-3β. Furthermore, in cultured PC12 cells, the same protective effects were also observed. Silencing Nrf2 gene with its siRNA abolished the Apelin 13’s prevention of I/R-induced PC12 cell injury, oxidative stress, and inflammation. Inhibition of AMPK by its siRNA decreased the level of Apelin 13-induced Nrf2 expression and diminished the protective effects of Apelin 13. The interplay relationship between GSK-3β and Nrf2 was also verified with relative overexpression. Using selective inhibitors, we further identified the upstream of AMPK/GSK-3β/Nrf2 is AR/Gα/PLC/IP3/CaMKK.ConclusionsIn conclusion, the previous results showed that Apelin 13 protected against I/R-induced ROS-mediated inflammation and oxidative stress through activating the AMPK/GSK-3β pathway by AR/Gα/PLC/IP3/CaMKK signaling, and further upregulated the expression of Nrf2-regulated antioxidant enzymes.

Our results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.