The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

Recent advances in hardware and software for mobile computing have enabled a new breed of mobile augmented reality (AR) systems and applications. A new breed of computing called ‘augmented ubiquitous computing' has resulted from the convergence of wearable computing, wireless networking, and mobile AR interfaces. In this paper, we provide a survey of different mobile and wireless technologies and how they have impact AR. Our goal is to place them into different categories so that it becomes easier to understand the state of art and to help identify new directions of research.(Wiley & Sons

Purpose To determine the feasibility of radiomic (computer extracted texture) features in differentiating radiation necrosis (RN) from recurrent brain tumors on routine MRI (Gadolinium (Gd)-T1w, T2w, FLAIR). Methods A retrospective study of brain tumor MRI obtained after 9-months (or later) post-radio-chemotherapy was collected from two institutions. In total, 58 patient studies were analyzed, consisting of a training (N = 43) cohort from one institution and an independent test (N = 15) cohort from another, with surgical histologic findings confirmed by an experienced neuropathologist at the respective institutions. Brain lesions on MRI were manually annotated by an expert neuro-radiologist. A set of radiomic features was extracted for every lesion on each MRI sequence: Gd-T1w, T2w, FLAIR. Feature selection was employed to identify the top 5 most discriminating features for every MRI sequence on the training cohort. These features were then evaluated on the test cohort via a support vector machine (SVM) classifier. The classification performance was compared against diagnostic reads by two expert neuro-radiologists, who had access to the same MRI sequences (Gd-T1w, T2w, and FLAIR) as the classifier. Results On the training cohort, the area under the receiver operating characteristic curve (AUC) was highest for FLAIR with 0.79, 95% CI [0.77, 0.81] for primary (N =22), and 0.79, 95% CI [0.75, 0.83], for metastatic subgroups (N = 21). Of the 15 studies in the holdout cohort, the SVM classifier identified 12 of 15 studies correctly, while neuro-radiologist 1 diagnosed 7 of 15, and neuro-radiologist 2 diagnosed 8 of 15 studies correctly, respectively. Discussion Our preliminary results appear to suggest that radiomic features may provide complementary diagnostic information on routine MRI sequences that may improve distinction of RN from recurrence, both for primary and metastatic brain tumors.

The chronic shortage of deceased kidney donors has led to increased utilization of donation after cardiac death (DCD) kidneys, the majority of which are procured in a controlled setting. The objective of this study is to evaluate transplantation outcomes from uncontrolled DCD (uDCD) donors and evaluate their utility as a source of donor kidneys. Concerted efforts should be focused on procurement of uDCD donors, which can provide another source of quality deceased donor kidneys.

No abstract