Non-invasive detection of glycine as a biomarker of malignancy in childhood brain tumours using in-vivo 1H MRS at 1.5 tesla confirmed by ex-vivo high-resolution magic-angle spinning NMR.

Management of brain tumours in children would benefit from improved non-invasive diagnosis, characterisation and prognostic biomarkers. Metabolite profiles derived from in-vivo MRS have been shown to provide such information. Studies indicate that using optimum a priori information on metabolite contents in the construction of linear combination (LC) models of MR spectra leads to improved metabolite profile estimation. Glycine (Gly) is usually neglected in such models due to strong overlap with myo-inositol (mI) and a low concentration in normal brain. However, biological studies indicate that Gly is abundant in high-grade brain tumours. This study aimed to investigate the quantitation of Gly in paediatric brain tumours using MRS analysed by LCModel, and its potential as a non-invasive biomarker of malignancy. Single-voxel MRS was performed using PRESS (TR 1500 ms, TE 30 ms/135 ms) on a 1.5 T scanner. Forty-seven cases (18 high grade (HG), 17 low grade (LG), 12 ungraded) were retrospectively selected if both short-TE and long-TE MRS (n = 33) or short-TE MRS and high-resolution magic-angle spinning (HRMAS) of matched surgical samples (n = 15) were available. The inclusion of Gly in LCModel analyses led to significantly reduced fit residues for both short-TE and long-TE MRS (p < 0.05). The Gly concentrations estimated from short-TE MRS were significantly correlated with the long-TE values (R = 0.91, p < 0.001). The Gly concentration estimated by LCModel was significantly higher in HG versus LG tumours for both short-TE (p < 1e-6) and long-TE (p = 0.003) MRS. This was consistent with the HRMAS results, which showed a significantly higher normalised Gly concentration in HG tumours (p < 0.05) and a significant correlation with the normalised Gly concentration measured from short-TE in-vivo MRS (p < 0.05). This study suggests that glycine can be reliably detected in paediatric brain tumours using in-vivo MRS on standard clinical scanners and that it is a promising biomarker of tumour aggressiveness.

Risk factors for readmission of inpatients with diabetes: A systematic review.

AIM: We have limited understanding of which risk factors contribute to increased readmission rates amongst people discharged from hospital with diabetes. We aim to complete the first review of its kind, to identify, in a systematic way, known risk factors for hospital readmission amongst people with diabetes, in order to better understand this costly complication. METHOD: The review was prospectively registered in the PROSPERO database. Risk factors were identified through systematic review of literature in PubMed, EMBASE & SCOPUS databases, performed independently by two authors prior to data extraction, with quality assessment and semi-quantitative synthesis according to PRISMA guidelines. RESULTS: Eighty-three studies were selected for inclusion, predominantly from the United States, and utilising retrospective analysis of local or regional data sets. 76 distinct statistically significant risk factors were identified across 48 studies. The most commonly identified risk factors were; co-morbidity burden, age, race and insurance type. Few studies conducted power calculations; unstandardized effect sizes were calculated for the majority of statistically significant risk factors. CONCLUSION: This review is important in assessing the current state of the literature and in supporting development of interventions to reduce readmission risk. Furthermore, it provides an important foundation for development of rigorous, pre-specified risk prediction models.

Clinical data integration model. Core interoperability ontology for research using primary care data.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance. OBJECTIVES: TRANSFoRm's general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care. METHODS: TRANSFoRm utilizes a unified structural / terminological interoperability framework, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care. RESULTS: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm's use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM. CONCLUSION: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

A multistage perceptual quality assessment for compressed digital angiogram images.

This paper describes a multistage perceptual quality assessment (MPQA) model for compressed images. The motivation for the development of a perceptual quality assessment is to measure (in)visible differences between original and processed images. The MPQA produces visible distortion maps and quantitative error measures informed by considerations of the human visual system (HVS). Original and decompressed images are decomposed into different spatial frequency bands and orientations modeling the human cortex. Contrast errors are calculated for each frequency and orientation, and masked as a function of contrast sensitivity and background uncertainty. Spatially masked contrast error measurements are then made across frequency bands and orientations to produce a single perceptual distortion visibility map (PDVM). A perceptual quality rating (PQR) is calculated from the PDVM and transformed into a one to five scale, PQR(1-5), for direct comparison with the mean opinion score, generally used in subjective ratings. The proposed MPQA model is based on existing perceptual quality assessment models, while it is differentiated by the inclusion of contrast masking as a function of background uncertainty. A pilot study of clinical experiments on wavelet-compressed digital angiogram has been performed on a sample set of angiogram images to identify diagnostically acceptable reconstruction. Our results show that the PQR(1-5) of diagnostically acceptable lossy image reconstructions have better agreement with cardiologists' responses than objective error measurement methods, such as peak signal-to-noise ratio A Perceptual thresholding and CSF-based Uniform quantization (PCU) method is also proposed using the vision models presented in this paper. The vision models are implemented in the thresholding and quantization stages of a compression algorithm and shown to produce improved compression ratio performance with less visible distortion than that of the embedded zerotrees wavelet (EZWs).

Reject analysis: a pilot programme for image quality management.

The radiographic film wastage and the different parameters affecting this wastage were analysed for a 9-week period at a 600-bed University Hospital. An overall reject rate of 7.6% was found. The different reasons for rejection were evaluated, while retake rate, relation between working experience of the personnel, amount of rejected films and total film wastage in surface (m2), were registered and analysed.

An intranet database for pacemaker patients.

A database system, incorporating smartcard technologies, was designed to hold the personal and pacing details of pacemaker patients, who attended a clinic at the Royal Sussex County Hospital (RSCH), Brighton, UK. Following an initial period of a 12 month clinical trial, with the database running on a standalone personal computer, the Pacemaker Patient Database has been redesigned and implemented as an intranet-based system. This paper describes the issues relating to the development of the new prototype system and identifies the design principles for intranet-based electronic health care (EHCR) record database systems.

WWW creates new interactive 3D graphics and collaborative environments for medical research and education.

Virtual Reality Modelling Language (VRML) is the start of a new era for medicine and the World Wide Web (WWW). Scientists can use VRML across the Internet to explore new three-dimensional (3D) worlds, share concepts and collaborate together in a virtual environment. VRML enables the generation of virtual environments through the use of geometric, spatial and colour data structures to represent 3D objects and scenes. In medicine, researchers often want to interact with scientific data, which in several instances may also be dynamic (e.g. MRI data). This data is often very large and is difficult to visualise. A 3D graphical representation can make the information contained in such large data sets more understandable and easier to interpret. Fast networks and satellites can reliably transfer large data sets from computer to computer. This has led to the adoption of remote tale-working in many applications including medical applications. Radiology experts, for example, can view and inspect in near real-time a 3D data set acquired from a patient who is in another part of the world. Such technology is destined to improve the quality of life for many people. This paper introduces VRML (including some technical details) and discusses the advantages of VRML in application developing.

(1)H magnetic resonance spectroscopy in the diagnosis of paediatric low grade brain tumours.

INTRODUCTION: Low grade gliomas are the commonest brain tumours in children but present in a myriad of ways, each with its own treatment challenges. Conventional MRI scans play an important role in their management but have limited ability to identify likely clinical behaviour. The aim of this study is to investigate (1)H magnetic resonance spectroscopy (MRS) as a method for detecting differences between the various low grade gliomas and related tumours in children. PATIENTS AND METHODS: Short echo time single voxel (1)H MRS at 1.5 or 3.0 T was performed prior to treatment on children with low grade brain tumours at two centres and five MR scanners, 69 cases had data which passed quality control. MRS data was processed using LCModel to give mean spectra and metabolite concentrations which were compared using T-tests, ANOVA, Receiver Operator Characteristic curves and logistic regression in SPSS. RESULTS: Significant differences were found in concentrations of key metabolites between glioneuronal and glial tumours (T-test p<0.05) and between most of the individual histological subtypes of low grade gliomas. The discriminatory metabolites identified, such as choline and myoinositol, are known tumour biomarkers. In the set of pilocytic astrocytomas and unbiopsied optic pathway gliomas, significant differences (p<0.05, ANOVA) were found in metabolite profiles of tumours depending on location and patient neurofibromatosis type 1 status. Logistic regression analyses yielded equations which could be used to assess the probability of a tumour being of a specific type. CONCLUSIONS: MRS can detect subtle differences between low grade brain tumours in children and should form part of the clinical assessment of these tumours.

Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS.

(1)H MRS has great potential for the clinical investigation of childhood brain tumours, but the low incidence in, and difficulties of performing trials on, children have hampered progress in this area. Most studies have used a long-TE, thus limiting the metabolite information obtained, and multivariate analysis has been largely unexplored. Thirty-five children with untreated cerebellar tumours (18 medulloblastomas, 12 pilocytic astrocytomas and five ependymomas) were investigated using a single-voxel short-TE PRESS sequence on a 1.5 T scanner. Spectra were analysed using LCModel to yield metabolite profiles, and key metabolite assignments were verified by comparison with high-resolution magic-angle-spinning NMR of representative tumour biopsy samples. In addition to univariate metabolite comparisons, the use of multivariate classifiers was investigated. Principal component analysis was used for dimension reduction, and linear discriminant analysis was used for variable selection and classification. A bootstrap cross-validation method suitable for estimating the true performance of classifiers in small datasets was used. The discriminant function coefficients were stable and showed that medulloblastomas were characterised by high taurine, phosphocholine and glutamate and low glutamine, astrocytomas were distinguished by low creatine and high N-acetylaspartate, and ependymomas were differentiated by high myo-inositol and glycerophosphocholine. The same metabolite features were seen in NMR spectra of ex vivo samples. Successful classification was achieved for glial-cell (astrocytoma + ependymoma) versus non-glial-cell (medulloblastoma) tumours, with a bootstrap 0.632 + error, e(B.632+), of 5.3%. For astrocytoma vs medulloblastoma and astrocytoma vs medulloblastoma vs ependymoma classification, the e(B.632+) was 6.9% and 7.1%, respectively. The study showed that (1)H MRS detects key differences in the metabolite profiles for the main types of childhood cerebellar tumours and that discriminant analysis of metabolite profiles is a promising tool for classification. The findings warrant confirmation by larger multi-centre studies.

Automated feature extraction for the classification of human in vivo 13C NMR spectra using statistical pattern recognition and wavelets.

If magnetic resonance spectroscopy (MRS) is to become a useful tool in clinical medicine, it will be necessary to find reliable methods for analyzing and classifying MRS data. Automated methods are desirable because they can remove user bias and can deal with large amounts of data, allowing the use of all the available information. In this study, techniques for automatically extracting features for the classification of MRS in vivo data are investigated. Among the techniques used were wavelets, principal component analysis, and linear discriminant function analysis. These techniques were tested on a set of 75 in vivo 13C spectra of human adipose tissue from subjects from three different dietary groups (vegan, vegetarian, and omnivore). It was found that it was possible to assign automatically 94% of the vegans and omnivores to their correct dietary groups, without the need for explicit identification or measurement of peaks.