Background: Magnetic resonance imaging (MRI) is increasingly being used to examine patients with suspected breast cancer. Purpose: To determine the diagnostic performance of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) for breast cancer detection. Material and Methods: A comprehensive search of the PUBMED, EMBASE, Web of Science, and Cochrane Library databases was performed up to September 2014. Statistical analysis included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs. Results: Fourteen studies were analyzed, which included a total of 1140 patients with 1276 breast lesions. The pooled sensitivity and specificity of combined DCE-MRI and DWI were 91.6% and 85.5%, respectively. The pooled sensitivity and specificity of DWI-MRI were 86.0% and 75.6%, respectively. The pooled sensitivity and specificity of DCE-MRI were 93.2% and 71.1%. The area under the SROC curve (AUC-SROC) of combined DCE-MRI and DWI was 0.94, the DCE-MRI of 0.85. Deeks testing confirmed no significant publication bias in all studies. Conclusion: Combined DCE-MRI and DWI had superior diagnostic accuracy than either DCE-MRI or DWI alone for the diagnosis of breast cancer.

Background: Increasing evidence suggests that the temporal and parietal lobes are associated with multisensory integration and vestibular migraine. However, temporal and parietal lobe structural and functional connectivity (FC) changes related to vestibular migraine need to be further investigated.Methods: Twenty-five patients with vestibular migraine (VM) and 27 age- and sex- matched healthy controls participated in this study. Participants completed standardized questionnaires assessing migraine and vertigo-related clinical features. Cerebral cortex characteristics [i.e., thickness (CT), fractal dimension (FD), sulcus depth (SD), and the gyrification index (GI)] were evaluated using an automated Computational Anatomy Toolbox (CAT12). Regions with significant differences were used in a seed-based comparison of resting-state FC conducted with DPABI. The relationship between changes in cortical characteristics or FC and clinical features was also analyzed in the patients with VM.Results: Relative to controls, patients with VM showed significantly thinner CT in the bilateral inferior temporal gyrus, left middle temporal gyrus, and the right superior parietal lobule. A shallower SD was observed in the right superior and inferior parietal lobule. FD and GI did not differ significantly between the two groups. A negative correlation was found between CT in the right inferior temporal gyrus, as well as the left middle temporal gyrus, and the Dizziness Handicap Inventory (DHI) score in VM patients. Furthermore, patients with VM exhibited weaker FC between the left inferior/middle temporal gyrus and the left medial superior frontal gyrus, supplementary motor area.Conclusion: Our data revealed cortical structural and resting-state FC abnormalities associated with multisensory integration, contributing to a lower quality of life. These observations suggest a role for multisensory integration in patients with VM pathophysiology. Future research should focus on using a task-based fMRI to measure multisensory integration.

Background: The thalamus has been reported to be associated with pain modulation and processing. However, the functional changes that occur in the thalamus of vestibular migraine (VM) patients remain unknown. Methods: In total, 28 VM patients and 28 healthy controls who were matched for age and sex underwent resting-state functional magnetic resonance imaging. They also responded to standardized questionnaires aimed at assessing the clinical features associated with migraine and vertigo. Differences in the amplitude of low-frequency fluctuation (ALFF) were analyzed and brain regions with altered ALFF in the two groups were used for further analysis of whole-brain functional connectivity (FC). The relationship between clusters and clinical features was investigated by correlation analyses. Results: The ALFF in the thalamus was significantly decreased in the VM group versus the control group. In the VM group, the ALFF in the left thalamus negatively correlated with VM episode frequency. Furthermore, the left thalamus showed significantly weaker FC than both regions of the medial prefrontal cortex, both regions of the anterior cingulum cortex, the left superior/middle temporal gyrus, and the left temporal pole in the VM group. Conclusions: The thalamus plays an important role in VM patients and it is suggested that connectivity abnormalities of the thalamocortical region contribute to abnormal pain information processing and modulation, transmission, and multisensory integration in patients with VM.

Objective: We aimed to reveal the role of structural and functional alterations of cingulate gyrus in early cognitive impairment in Type 2 diabetes mellitus (T2DM) patients.Methods: Fifty‐six T2DM patients and 60 healthy controls (HCs) underwent a neuropsychological assessment and sagittal three‐dimensional T1‐weighted and resting‐state functional MRI. Differences in the cortical thickness of the cingulate cortex and the functional connectivity (FC) of the nine subregions of the cingulate gyrus and the whole brain were compared between T2DM patients and HCs. Correlation analysis was performed between cortex thickness and FC and the participants' clinical/cognitive variables.Results: The cortical thickness of the cingulate gyrus was not significantly different between T2DM patients and HCs. However, the T2DM patients showed significantly lower FC between the pregenual ACC (pACC) and the bilateral hippocampus, significantly higher FC between the pACC and bilateral lateral prefrontal cortex (LPFC) and left precentral gyrus, and significantly lower FC between the retrosplenial cortex (RSC) and right cerebellar Crus I. The FC between the pACC and the left hippocampus was negatively correlated with the FC between the pACC and LPFC (r = −0.306, p = 0.022).Interpretation: The pACC and the RSC show dysfunctional connectivity before the appearance of structural abnormalities in T2DM patients. Abnormal FC of the pACC with the bilateral hippocampus and LPFC may imply a neural compensatory mechanism for memory function. These findings provide valuable information and new directions for possible interventions for the T2DM‐related cognitive impairment.

Objective: To develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS).Methods: The data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training cohort (n = 110) and a validation cohort (n = 46). Baseline clinical data were collected from our medical records, and plaque radiological features were extracted from vascular wall high-resolution imaging (VW-HRMRI). The imaging sequences included 3D-T1WI-VISTA, T2WI, and 3D-T1WI-VISTA-enhanced imaging. Least absolute shrinkage and selection operator (LASSO) analysis were used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures were performed, and a predictive nomogram was constructed to predict the probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated.Results: Diabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.04–3.79, p = 0.018; OR = 1.76, per 10% increase, 95% CI, 1.28–2.41, p < 0.001; and OR = 1.94, 95% CI: 1.27–3.09, p < 0.001]. Five features of 3D-T1WI-VISTA, six features of T2WI, and nine features of 3D-T1WI-VISTA-enhanced images were associated with stroke recurrence. The radiomics signature in 3D-T1WI-VISTA-enhanced images was superior to the radiomics signature of the other two sequences for predicting stroke recurrence in both the training cohort [area under the curve (AUC), 0.790, 95% CI: 0.669–0.894] and the validation cohort (AUC, 0.779, 95% CI: 0.620–0.853). The combination of clinical risk factors, radiological features, and radiomics signature had the best predictive value (AUC, 0.899, 95% CI: 0.844–0.936 in the training cohort; AUC, 0.803, 95% CI: 0.761–0.897 in the validation cohort). The C-index of the nomogram was 0.880 (95% CI: 0.805–0.934) and 0.817 (95% CI: 0.795–0.948), respectively, in the training and validation cohorts. The decision curve analysis further confirmed that the radiomics nomogram had good clinical applicability with a net benefit of 0.458.Conclusion: The radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.