ObjectiveTo systematically review the value of radiomics in the diagnosis of glioblastoma. MethodsPubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect studies on radiomics in the grading of gliomas or the differentiation diagnosis from inception to May 30th, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias and the quality of the included studies. Meta-analysis was then performed using Meta-Disc 1.4 software and RevMan 5.3 software. ResultsA total of 37 studies involving 2 746 subjects were included. The results of meta-analysis showed that the pooled sensitivity, specificity, and diagnostic odds ratio for the diagnosis of glioblastoma by radiomics were 0.91 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.90), and 78.00 (95%CI 50.81 to 119.72), respectively. The area under the summary receiver operating characteristic (SROC) curve was 0.95. The key radiomic features for correct diagnosis of glioblastoma included intensity features and texture features of the lesions. ConclusionThe current evidence shows that radiomics provides good diagnostic accuracy for glioblastoma. Due to the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.
Red blood cells are destroyed when the shear stress in the blood pump exceeds a threshold, which in turn triggers hemolysis in the patient. The impeller design of centrifugal blood pumps significantly influences the hydraulic characteristics and hemolytic properties of these devices. Based on this premise, the present study employs a multiphase flow approach to numerically simulate centrifugal blood pumps, investigating the performance of pumps with varying numbers of blades and blade deflection angles. This analysis encompassed the examination of flow field characteristics, hydraulic performance, and hemolytic potential. Numerical results indicated that the concentration of red blood cells and elevated shear stresses primarily occurred at the impeller and volute tongue, which drastically increased the risk of hemolysis in these areas. It was found that increasing the number of blades within a certain range enhanced the hydraulic performance of the pump but also raised the potential for hemolysis. Moreover, augmenting the blade deflection angle could improve the hemolytic performance, particularly in pumps with a higher number of blades. The findings from this study can provide valuable insights for the structural improvement and performance enhancement of centrifugal blood pumps.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.