Objective:Accurate and noninvasive pathologic grading of glioma patients before surgery was crucial to guiding clinicians to select appropriate treatment and improve patient prognosis. This study was performed to investigate the potential diagnostic value of diffusion kurtosis imaging (DKI) to distinguish high-grade gliomas (HGGs) from low-grade gliomas (LGGs) based on an evidence-based approach.Methods:Relevant articles that used DKI to distinguish HGG from LGG in Embase, PubMed, China Knowledge Resource Integrated database (CNKI), Web of Knowledge, and Cochrane Libraries databases were electronically searched to April 31, 2018 by 2 reviewers. All analysis was performed by using Meta-disc1.4 and Stata. Influence factors on the diagnostic accuracy were evaluated using meta-regression analysis.Results:Five eligible studies were included in this meta-analysis. The pooled sensitivity (SEN) and specificity (SPE) was 91% (confidence interval [CI]: 0.78–0.96; P = .02) and 91% (CI: 0.80–0.97; P = .01). The pooled data showed that diagnostic odds ratio (DOR) of DKI was 79.75 (CI: 31.57–201.45). The area under the curve (AUC) of summary receiver operating characteristic curve was 0.96. There is no evidence that our research has a threshold effect (Spearman correlation coefficient: 0.300, P = .624) and publication bias. Meta regression analysis identified that country, language, field strength, and parameter of magnetic resonance imaging had no significant effect on diagnostic performance.Conclusion:The present meta-analysis shows that the mean kurtosis values derived from DKI may be useful in characterization of gliomas with high sensitivity and specificity. Taken into consideration the small sample of this study, we need to be cautious when interpreting the results of this study.
Objective: To study the characteristics of the multimodal functional Magnetic Resonance Imaging (fMRI) of schizophrenia patients with initial negative symptom. Methods: From January 2014 to January 2016, 30 cases of schizophrenia patients with the initial negative symptom and 30 cases of schizophrenia patients with the initial positive symptom were involved in our study. Besides, other 30 cases were matched into the healthy control based on the gender, age and education level. The 3D structure Magnetic Resonance Imaging (MRI) data and functional MRI data of all patients were collected. The voxel-based morphometry was used to analyse different brain regions in structure MRI data among those three groups. In addition, we analysed the correlation between all functions of the whole brain in the resting state and different brain regions. Results: The volume of the gray matter in the right dorsolateral prefrontal lobe of the patients with initial negative symptom was significantly higher than that of patients with initial positive symptom. Moreover, in the control group, the whole brain's functional connectivity was decreased in the dorsolateral prefrontal cortex under a resting state. The difference was mainly on functional connectivity between the right dorsolateral prefrontal cortex-right medial prefrontal cortex and right dorsolateral prefrontal cortex-right caudate nucleus. The Z value of right medial prefrontal cortex in the negative symptom group was significantly lower than those of other two groups. The right caudate nucleus Z value was significantly higher than those of other two groups. When the N-back working memory task was given, the different brain regions in activations among the three groups were the left dorsolateral prefrontal cortex, medial prefrontal cortex/anterior cingulated cortex and posterior cingulated cortex, the inhibition degree against the medial prefrontal cortex/anterior cingulated cortex and posterior cingulated cortex of patients with the initial negative symptom as primary type was significantly decreased and the dorsolateral prefrontal activation was significantly increased. Conclusion: It has a significant value to diagnose and identify the schizophrenia with the initial negative symptom through the characteristics of the multimodal fMRI.
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