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The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used., (Copyright © 2015 John Wiley & Sons, Ltd.)

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.

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.