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Brain tumours are the most frequently occuring solid tumours affecting childhood, representing 27% of all cancers. The most common posterior fossa tumours are medulloblastoma, pilocytic astrocytoma and ependymoma. Texture Analysis (TA) of Magnetic Resonance Imaging (MRI) aims to represent pixel distributions, intensities and dependencies using mathematically defined features. Such features could potentially provide quantifiable information that is beyond the human vision capabilities, and hence be used to supplement qualitative assessments conducted by radiologists. The primary aim of this study was to carry out a multicentre investigation on the efficacy of 3D TA for diagnostic classification of childhood brain tumours, using conventional MRI images. The data used had been acquired at three different hospitals and consisted of pre-contrast T1 and T2-weighted MRI series, obtained from 121 children diagnosed with medulloblastoma, pilocytic astrocytoma and ependymoma. Using 3D textural features, based on first, second and higher order statistical methods, a support vector machine (SVM) classifier was trained and tested using the leave-one-out cross-validation (LOOCV) approach. An essential outcome of this study is that 3D TA demonstrated a good overall performance, when used on data acquired from a number of centres and using scanners made by different manufacturers and at different magnetic field strengths.

Novel imaging techniques are playing an increasing role in tumour characterisation, assessment and management. However, incorporating imaging data into clinical trials presents a number of challenges in terms of quality control, standardisation in data collection, interoperability of widely used archiving systems and extensibility of imaging software architectures. Additionally, currently available monolithic applications cannot fulfil the diverse and rapidly changing needs of the clinical imaging research community. This paper discusses the limitations of the current CCLG Remote Data Entry (RDE) system and introduces the prototype of an alternative modular system based on the Extensible Neuroimaging Archive Toolkit (XNAT). The modular nature of the presented prototype promotes incremental software evolution and allows for flexible system customisation to suit the needs of individual imaging centres.

In order to explore the role of social media in forming an understanding of digital healthcare, we conducted a study involving sentiment and network analysis of Twitter contents. In doing this, we gathered 20,400 tweets that mentioned the key term #DigitalHealth for 55 hours, over a three-day period. In addition to examining users' opinions through sentiment analysis, we calculated in-degree centralities of nodes to identify the hubs in the network of interactions. The results suggest that the overall opinion about digital healthcare is generally positive. Additionally, our findings indicate that the most prevalent keywords, associated with digital health, widely range from mobile health to wearable technologies and big data. Surprisingly, the results show that the newly announced wearable technologies could occupy the majority of discussions.

Brain and central nervous system (CNS) tumours form the second most common group of cancers in children in the UK, accounting for 27% of all childhood cancers. Initial assessment of tumours from MRI scans is usually performed qualitatively, via radiologists' visual inspection. However, different brain tumours do not always demonstrate clear differences in physical appearance, so a diagnosis is usually made via histopathological examination of biopsy samples taken through surgery. This gives rise to the need for accurate, yet non-invasive diagnostic aids. In a previous study, we demonstrated the potential of MRI texture analysis in capturing quantitative information about paediatric brain tumours. In this work, we carry out a preliminary investigation on the use of 3D (volumetric) texture analysis of T1 and T2-weighted MR images in order to classify paediatric brain tumours. We then compare its performance with the traditional 2D texture analysis approach. Our preliminary findings are very encouraging and show that 3D textural features are capable of capturing more discriminative information about the tumours than the traditional 2D approach. However, it remains necessary to expand the work further to include larger cohorts and additional modalities.

Despite current advances in neuro-imaging, the characterisation of paediatric brain tumours and neurological disorders is very challenging. Whilst Magnetic Resonance Imaging (MRI) provides images of superb clarity, it gives little information on how aggressive a tumour is. It is also very difficult to visually inspect any underlying textural patterns between tumours on MR images. This gives rise to the need for a quantitative means of analysing MR images for characterising tumours. In this work, we present a preliminary investigation into the effectiveness of texture analysis as a quantitative approach for classifying paediatric brain tumours.