Purpose:To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival. Materials and Methods:Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff a statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test. Results:Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P , .01). Conclusion:This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.q RSNA, 2013

Compared with the MMSE, the MoCA-P is significantly better for detecting MCI in the elderly, particularly in the oldest old population, and it also displays more effectiveness in detecting dementia.

BackgroundCerebrovascular lesions are a frequent finding in the elderly population. However, the impact of these lesions on cognitive performance, the prevalence of vascular dementia, and the pathophysiology behind characteristic in vivo imaging findings are subject to controversy. Moreover, there are no standardised criteria for the neuropathological assessment of cerebrovascular disease or its related lesions in human post-mortem brains, and conventional histological techniques may indeed be insufficient to fully reflect the consequences of cerebrovascular disease.DiscussionHere, we review and discuss both the neuropathological and in vivo imaging characteristics of cerebrovascular disease, prevalence rates of vascular dementia, and clinico-pathological correlations. We also discuss the frequent comorbidity of cerebrovascular pathology and Alzheimer’s disease pathology, as well as the difficult and controversial issue of clinically differentiating between Alzheimer’s disease, vascular dementia and mixed Alzheimer’s disease/vascular dementia. Finally, we consider additional novel approaches to complement and enhance current post-mortem assessment of cerebral human tissue.ConclusionElucidation of the pathophysiology of cerebrovascular disease, clarification of characteristic findings of in vivo imaging and knowledge about the impact of combined pathologies are needed to improve the diagnostic accuracy of clinical diagnoses.