Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study.

Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.

Brain Re-Irradiation Or Chemotherapy: a phase II randomised trial of re-irradiation and chemotherapy in patients with recurrent glioblastoma (BRIOChe) - protocol for a multi-centre open-label randomised trial.

INTRODUCTION: Glioblastoma (GBM) is the most common adult primary malignant brain tumour. The condition is incurable and, despite aggressive treatment at first presentation, almost all tumours recur after a median of 7 months. The aim of treatment at recurrence is to prolong survival and maintain health-related quality of life (HRQoL). Chemotherapy is typically employed for recurrent GBM, often using nitrosourea-based regimens. However, efficacy is limited, with reported median survivals between 5 and 9 months from recurrence. Although less commonly used in the UK, there is growing evidence that re-irradiation may produce survival outcomes at least similar to nitrosourea-based chemotherapy. However, there remains uncertainty as to the optimum approach and there is a paucity of available data, especially with regards to HRQoL. Brain Re-Irradiation Or Chemotherapy (BRIOChe) aims to assess re-irradiation, as an acceptable treatment option for recurrent IDH-wild-type GBM. METHODS AND ANALYSIS: BRIOChe is a phase II, multi-centre, open-label, randomised trial in patients with recurrent GBM. The trial uses Sargent's three-outcome design and will recruit approximately 55 participants from 10 to 15 UK radiotherapy sites, allocated (2:1) to receive re-irradiation (35 Gy in 10 daily fractions) or nitrosourea-based chemotherapy (up to six, 6-weekly cycles). The primary endpoint is overall survival rate for re-irradiation patients at 9 months. There will be no formal statistical comparison between treatment arms for the decision-making primary analysis. The chemotherapy arm will be used for calibration purposes, to collect concurrent data to aid interpretation of results. Secondary outcomes include HRQoL, dexamethasone requirement, anti-epileptic drug requirement, radiological response, treatment compliance, acute and late toxicities, progression-free survival. ETHICS AND DISSEMINATION: BRIOChe obtained ethical approval from Office for Research Ethics Committees Northern Ireland (reference no. 20/NI/0070). Final trial results will be published in peer-reviewed journals and adhere to the ICMJE guidelines. TRIAL REGISTRATION NUMBER: ISRCTN60524.

Glioblastoma and Radiotherapy: a multi-center AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at eight months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, IDH-wildtype patients diagnosed between March 2014-February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from three centers. Holdout test sets were retrospective (n=19; internal validation), and prospective (n=29; external validation from eight distinct centers).Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A non-imaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; non-imaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the non-imaging model in all test sets (area under the receiver-operating characteristic curve, AUC p=0.038) and performed similarly to a combined imaging/non-imaging model (p>0.05). Imaging, non-imaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10,000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; p=0.003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy, reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Consistency of Radiological Grading of Cervical Foraminal Stenosis.

BACKGROUND: The degree of cervical foraminal stenosis on MRI scans may be measured and categorised using the Kim or modified Kim methods. These grading scales have not previously been validated in a cohort of patients awaiting surgery. OBJECTIVES: To establish the normal foraminal and root diameters as well as the consistency of inter and intra-rater grading using the Kim and modified Kim grading systems in pre-operative surgical patients. METHODS: Asymptomatic cervical nerve roots and foramina demonstrated on the pre-operative MRI scans of adult surgical patients with cervical radiculopathy were measured and categorised by six raters using the Kim and modified Kim grading methods. Repeat "second pass" measurements were made by the same assessors on the same images a minimum of one month later. RESULTS: Foraminal diameters (mm) in asymptomatic foramina were C2/C3 (mean±SD): 4.18±1.44, C3/C4 2.96±1.23, C4/C5 3.02±1.19, C5/C6 3.15±1.33, C6/C7 3.53±1.36, C7/T1 3.93±1.34. Nerve root diameters were C3 3.11±0.87, C4 2.95±0.77, C5 2.56±0.73, C6 2.26±0.76, C7 2.56±0.82, C8 3.83±0.86. Inter-rater consistency was kappa [95% CI]: Kim 0.01 [0.00, 0.03], modified Kim 0.08 [0.05, 0.10]. Intra-rater consistency was kappa [95% CI]: Kim 0.81 [0.77, 0.86], modified Kim 0.69 [0.62, 0.76]. CONCLUSION: There was poor inter-rater consistency but good intra-rater consistency when assessing the severity of foraminal stenosis on axial T2 MRI scans. Foraminal diameter was narrowest at C3/C4 and C4/C5, whereas the smallest root diameter was C5/C6. Volumetric or oblique MR may improve consistency.

Imaging Spectrum of the Developing Glioblastoma: A Cross-Sectional Observation Study.

Glioblastoma (GBM) has the typical radiological appearance (TRA) of a centrally necrotic, peripherally enhancing tumor with surrounding edema. The objective of this study was to determine whether the developing GBM displays a spectrum of imaging changes detectable on routine clinical imaging prior to TRA GBM. Patients with pre-operative imaging diagnosed with GBM (1 January 2014-31 March 2022) were identified from a neuroscience center. The imaging was reviewed by an experienced neuroradiologist. Imaging patterns preceding TRA GBM were analyzed. A total of 76 out of 555 (14%) patients had imaging preceding TRA GBM, 57 had solitary lesions, and 19 had multiple lesions (total = 84 lesions). Here, 83% of the lesions had cortical or cortical/subcortical locations. The earliest imaging features for 84 lesions were T2 hyperintensity/CT low density (n = 18), CT hyperdensity (n = 51), and T2 iso-intensity (n = 15). Lesions initially showing T2 hyperintensity/CT low density later showed T2 iso-intensity. When CT and MRI were available, all CT hyperdense lesions showed T2 iso-intensity, reduced diffusivity, and the following enhancement patterns: nodular 35%, solid 29%, none 26%, and patchy peripheral 10%. The mean time to develop TRA GBM from T2 hyperintensity was 140 days and from CT hyperdensity was 69 days. This research suggests that the developing GBM shows a spectrum of imaging features, progressing through T2 hyperintensity to CT hyperdensity, T2 iso-intensity, reduced diffusivity, and variable enhancement to TRA GBM. Red flags for non-TRA GBM lesions are cortical/subcortical CT hyperdense/T2 iso-intense/low ADC. Future research correlating this imaging spectrum with pathophysiology may provide insight into GBM growth patterns.

Do animal models of brain tumors replicate human peritumoral edema? a systematic literature search.

INTRODUCTION: Brain tumors cause morbidity and mortality in part through peritumoral brain edema. The current main treatment for peritumoral brain edema are corticosteroids. Due to the increased recognition of their side-effect profile, there is growing interest in finding alternatives to steroids but there is little formal study of animal models of peritumoral brain edema. This study aims to summarize the available literature. METHODS: A systematic search was undertaken of 5 literature databases (Medline, Embase, CINAHL, PubMed and the Cochrane Library). The generic strategy was to search for various terms associated with "brain tumors", "brain edema" and "animal models". RESULTS: We identified 603 reports, of which 112 were identified as relevant for full text analysis that studied 114 peritumoral brain edema animal models. We found significant heterogeneity in the species and strain of tumor-bearing animals, tumor implantation method and edema assessment. Most models did not produce appreciable brain edema and did not test for observable manifestations thereof. CONCLUSION: No animal model currently exists that enable the investigation of novel candidates for the treatment of peritumoral brain edema. With current interest in alternative treatments for peritumoral brain edema, there is an unmet need for clinically relevant animal models.

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Liquid biopsies for early diagnosis of brain tumours: in silico mathematical biomarker modelling.

Brain tumours are the biggest cancer killer in those under 40 and reduce life expectancy more than any other cancer. Blood-based liquid biopsies may aid early diagnosis, prediction and prognosis for brain tumours. It remains unclear whether known blood-based biomarkers, such as glial fibrillary acidic protein (GFAP), have the required sensitivity and selectivity. We have developed a novel in silico model which can be used to assess and compare blood-based liquid biopsies. We focused on GFAP, a putative biomarker for astrocytic tumours and glioblastoma multi-formes (GBMs). In silico modelling was paired with experimental measurement of cell GFAP concentrations and used to predict the tumour volumes and identify key parameters which limit detection. The average GBM volumes of 449 patients at Leeds Teaching Hospitals NHS Trust were also measured and used as a benchmark. Our model predicts that the currently proposed GFAP threshold of 0.12 ng ml-1 may not be suitable for early detection of GBMs, but that lower thresholds may be used. We found that the levels of GFAP in the blood are related to tumour characteristics, such as vasculature damage and rate of necrosis, which are biological markers of tumour aggressiveness. We also demonstrate how these models could be used to provide clinical insight.

Exploratory Analysis of Serial 18F-fluciclovine PET-CT and Multiparametric MRI during Chemoradiation for Glioblastoma.

Anti-1-amino-3-18fluorine-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography (PET) shows preferential glioma uptake but there is little data on how uptake correlates with post-contrast T1-weighted (Gd-T1) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) activity during adjuvant treatment. This pilot study aimed to compare 18F-fluciclovine PET, DCE-MRI and Gd-T1 in patients undergoing chemoradiotherapy for glioblastoma (GBM), and in a parallel pre-clinical GBM model, to investigate correlation between 18F-fluciclovine uptake, MRI findings, and tumour biology. 18F-fluciclovine-PET-computed tomography (PET-CT) and MRI including DCE-MRI were acquired before, during and after adjuvant chemoradiotherapy (60 Gy in 30 fractions with temozolomide) in GBM patients. MRI volumes were manually contoured; PET volumes were defined using semi-automatic thresholding. The similarity of the PET and DCE-MRI volumes outside the Gd-T1 volume boundary was measured using the Dice similarity coefficient (DSC). CT-2A tumour-bearing mice underwent MRI and 18F-fluciclovine PET-CT. Post-mortem mice brains underwent immunohistochemistry staining for ASCT2 (amino acid transporter), nestin (stemness) and Ki-67 (proliferation) to assess for biologically active tumour. 6 patients were recruited (GBM 1-6) and grouped according to overall survival (OS)-short survival (GBM-SS, median OS 249 days) and long survival (GBM-LS, median 903 days). For GBM-SS, PET tumour volumes were greater than DCE-MRI, in turn greater than Gd-T1. For GBM-LS, Gd-T1 and DCE-MRI were greater than PET. Tumour-specific 18F-fluciclovine uptake on pre-clinical PET-CT corresponded to immunostaining for Ki-67, nestin and ASCT2. Results suggest volumes of 18F-fluciclovine-PET activity beyond that depicted by DCE-MRI and Gd-T1 are associated with poorer prognosis in patients undergoing chemoradiotherapy for GBM. The pre-clinical model confirmed 18F-fluciclovine uptake reflected biologically active tumour.

Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders.

Background and purpose: Magnetic Resonance Imaging (MRI) exhibits scanner dependent contrast, which limits generalisability of radiomics and machine-learning for radiation oncology. Current deep-learning harmonisation requires paired data, retraining for new scanners and often suffers from geometry-shift which alters anatomical information. The aim of this study was to investigate style-blind auto-encoders for MRI harmonisation to accommodate unpaired training data, avoid geometry-shift and harmonise data from previously unseen scanners. Materials and methods: A style-blind auto-encoder, using adversarial classification on the latent-space, was designed for MRI harmonisation. The public CC359 T1-w MRI brain dataset includes six scanners (three manufacturers, two field strengths), of which five were used for training. MRI from all six (including one unseen) scanner were harmonised to common contrast. Harmonisation extent was quantified via Kolmogorov-Smirnov testing of residual scanner dependence of 3D radiomic features, and compared to WhiteStripe normalisation. Anatomical content preservation was measured through change in structural similarity index on contrast-cycling (δSSIM). Results: The percentage of radiomics features showing statistically significant scanner-dependence was reduced from 41% (WhiteStripe) to 16% for white matter and from 39% to 27% for grey matter. δSSIM < 0.0025 on harmonisation and de-harmonisation indicated excellent anatomical content preservation. Conclusions: Our method harmonised MRI contrast effectively, preserved critical anatomical details at high fidelity, trained on unpaired data and allowed zero-shot harmonisation. Robust and clinically translatable harmonisation of MRI will enable generalisable radiomic and deep-learning models for a range of applications, including radiation oncology treatment stratification, planning and response monitoring.

Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review.

OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. METHODS: MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. RESULTS: After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. CONCLUSION: Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required. KEY POINTS: • Intensity standardisation is a key pre-processing step in the development of robust radiomic signatures to evaluate diffuse glioma. • A minority of studies compared the impact of two or more methods. • Further research is required to directly compare multiple methods of MRI intensity standardisation on glioma datasets.

Volumetric and dosimetric impact of post-surgical MRI-guided radiotherapy for glioblastoma: A pilot study.

Objectives: Glioblastoma (GBM) radiotherapy (RT) target delineation requires MRI, ideally concurrent with CT simulation (pre-RT MRI). Due to limited MRI availability, <72 h post-surgery MRI is commonly used instead. Whilst previous investigations assessed volumetric differences between post-surgical and pre-RT delineations, dosimetric impact remains unknown. We quantify volumetric and dosimetric impact of using post-surgical MRI for GBM target delineation. Methods: Gross tumour volumes (GTVs) for five GBM patients receiving chemo-RT with post-surgical and pre-RT MRIs were delineated by three independent observers. Planning target volumes (PTVs) and RT plans were generated for each GTV. Volumetric and dosimetric differences were assessed through: absolute volumes, volume-distance histograms and dose-volume histogram statistics. Results: Post-surgical MRI delineations had significantly (p < 0.05) larger GTV and PTV volumes (median 16.7 and 64.4 cm3, respectively). Post-surgical RT plans, applied to pre-RT delineations, had significantly decreased (p < 0.01) median PTV doses (ΔD99% = -8.1 Gy and ΔD95% = -2.0 Gy). Median organ-at-risk (OAR) dose increases (brainstem ΔD5% =+0.8, normal brain mean dose =+2.9 and normal brain ΔD10% = 5.3 Gy) were observed. Conclusion: Post-surgical MRI delineation significantly impacted RT planning, with larger normal-appearing tissue volumes irradiated and increased OAR doses, despite a reduced coverage of the pre-RT defined target. Advances in knowledge: We believe this is the first investigation assessing the dosimetric impact of using post-surgical MRI for GBM target delineation. It highlights the potential of significantly degraded RT plans, showing the clinical need for dedicated MRI for GBM RT.

Post-treatment FDG PET-CT in head and neck carcinoma: comparative analysis of 4 qualitative interpretative criteria in a large patient cohort.

There is no consensus regarding optimal interpretative criteria (IC) for Fluorine-18 fluorodeoxyglucose (FDG) Positron Emission Tomography - Computed Tomography (PET-CT) response assessment following (chemo)radiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC). The aim was to compare accuracy of IC (NI-RADS, Porceddu, Hopkins, Deauville) for predicting loco-regional control and progression free survival (PFS). All patients with histologically confirmed HNSCC treated at a specialist cancer centre with curative-intent non-surgical treatment who underwent baseline and response assessment FDG PET-CT between August 2008 and May 2017 were included. Metabolic response was assessed using 4 different IC harmonised into 4-point scales (complete response, indeterminate, partial response, progressive disease). IC performance metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy) were compared. Kaplan-Meier and Cox proportional hazards regression analyses were performed for survival analysis. 562 patients were included (397 oropharynx, 53 hypopharynx, 48 larynx, 64 other/unknown primary). 420 patients (75%) received CRT and 142 (25%) had radiotherapy alone. Median follow-up was 26 months (range 3-148). 156 patients (28%) progressed during follow-up. All IC were accurate for prediction of primary tumour (mean NPV 85.0% (84.6-85.3), PPV 85.0% (82.5-92.3), accuracy 84.9% (84.2-86.0)) and nodal outcome (mean NPV 85.6% (84.1-86.6), PPV 94.7% (93.8-95.1), accuracy 86.8% (85.6-88.0)). Number of indeterminate scores for NI-RADS, Porceddu, Deauville and Hopkins were 91, 25, 20, 13 and 55, 70, 18 and 3 for primary tumour and nodes respectively. PPV was significantly reduced for indeterminate uptake across all IC (mean PPV primary tumour 36%, nodes 48%). Survival analyses showed significant differences in PFS between response categories classified by each of the four IC (p <0.001). All four IC have similar diagnostic performance characteristics although Porceddu and Deauville scores offered the best trade off of minimising indeterminate outcomes whilst maintaining a high NPV.

Multiple myeloma with multiple neurological presentations.

Multiple myeloma is a haematological malignancy with clonal plasma cell proliferation and production of monoclonal immunoglobulins. Its neurological complications are relatively common, caused by both the disease and the treatment. Neurologists should therefore be familiar with its neurological manifestations and complications. We describe a 40-year-old woman who presented with lower cranial neuropathies mimicking variant Guillain-Barré syndrome, with normal brain and spinal cord imaging and cerebrospinal fluid (CSF) albuminocytological dissociation, and subsequently diagnosed with IgD myeloma. She relapsed repeatedly with differing neurological presentations: numb chin syndrome and twice with impaired vision, first from cerebral venous sinus thrombosis and later from leptomeningeal infiltration of the optic chiasm. We discuss the neurological complications of myeloma, emphasising the need to consider it in a wide variety of neurological presentations and repeatedly to reassess its associated neurological diagnoses. We also highlight the complexity of myeloma treatment.

Multiple system atrophy mimicked by multi-organ pathology.

Both multiple system atrophy and Parkinson's disease may present with parkinsonism and autonomic dysfunction. We describe a patient who initially met the diagnostic criteria for multiple system atrophy and had atypical features for Parkinson's disease including blackouts and pyramidal signs. Ultimately, he was found to have three separate diagnoses rather than a single unifying one.

Should we be moving to a national standardized non-gadolinium MR imaging protocol for the surveillance of vestibular schwannomas?

OBJECTIVES:: To examine whether the model of Getting It Right First Time (GIRFT) could be relevant to the surveillance of non-operated vestibular schwannomas (vs) by testing the following hypotheses: (1) in the UK there is a great variation in the imaging protocol for the follow-up of vs; (2) high-resolution, T 2 weighted MRI (HRT 2W-MRI) has an equivalent accuracy to gadolinium-enhanced T 1 weighted MRI (Gd-MRI) in the assessment of vs size and; (3) imaging with HRT 2W-MRI rather than Gd-MRI could offer financial savings. METHODS:: Two neuroradiologists independently performed measurements of 50 vs imaged with HRT 2W-MRI and Gd-MRI. Differences in mean tumour measurements between HRT 2W-MRI and Gd-MRI were determined, as were intra- and interobserver concordance. Level of agreement was measured using Bland-Altman plots. Consultant neuroradiologists within 30 adult neurosurgical units in the UK were contacted via email and asked to provide the MRI protocol used for the surveillance of non-operated vs in their institution. The financial difference between scanning with HRT 2W-MRI and Gd-MRI was determined within Leeds Teaching Hospitals NHS Trust. RESULTS:: There was no statistically significant difference in the mean diameter of vs size, measured on HRT 2W-MRI and Gd-MRI (p = 0.28 & p = 0.74 for observers 1 and 2 respectively). Inter- and intraobserver concordance were excellent (Interclass correlation coefficient = 0.99, Interclass correlation coefficient ≥ 0.98 respectively). Differences between the two sequences were within limits of agreement. 26 of 30 UK neuroscience centres (87 % response rate) provided imaging protocols. 16 of the 26 (62%) centres use Gd-MRI for the surveillance of vs. HRT 2-MRI is £36.91 cheaper per patient than Gd-MRI. CONCLUSION:: Variation exits across UK centres in the imaging surveillance of non-operated vs. HRT 2W-MRI and Gd-MRI have equivalent accuracy when measuring vs. Imaging with HRT 2W-MRI rather than Gd-MRI offers potential financial savings. ADVANCES IN KNOWLEDGE:: This study highlights the potential health and economic benefits of a national standardized imaging protocol for the surveillance of non-operated vs.

Magnetic resonance spectroscopy of the brain.

Proton magnetic resonance (MR) spectroscopy of the brain is a non-invasive, in vivo technique that allows investigation into regional chemical environments. Its complementary use with MR imaging sequences provides valuable insights into brain tumour characteristics, progression and response to treatment. Additionally, its sensitivity to brain dysfunction in the presence of apparently normal structural imaging has galvanised interest in its use as a biomarker of neurodegenerative disorders such as Alzheimer's disease. Accordingly, its integration into clinical imaging protocols within many neuroscience centres throughout the world is increasing. This growing attention is encouraging but if the potential of MR spectroscopy is to be realised, fundamental questions need to be addressed, such as reproducibility of the technique and the biochemistry that underpins the neurometabolites measured. Failure to resolve these issues will continue to hinder the extent and accuracy of conclusions that can be drawn from its data. In this review we discuss the issues regarding MR spectroscopy in the brain with particular attention paid to its technique. Key examples of current clinical applications are provided and future directions are discussed.

Should we be 'nervous' about coeliac disease? Brain abnormalities in patients with coeliac disease referred for neurological opinion.

OBJECTIVES: To examine the extent of brain abnormality in patients with coeliac disease referred for neurological opinion and evaluate MR imaging sequences as biomarkers for neurological dysfunction, given the lack of readily available serological markers of neurological disease in this cohort. METHODS: Retrospective examination of a consecutive cohort of patients (n = 33, mean age = 44 ± 13 years (range 19-64)) with biopsy proven coeliac disease referred for neurological opinion. Patients were divided into subgroups based on their primary neurological complaint (balance disturbance, headache and sensory loss). 3T MR was used to evaluate differences in brain grey matter density, cerebellar volume, cerebellar neurochemistry and white matter abnormalities (WMAs) between subjects and controls. RESULTS: Cerebellar volume was significantly less in the patient group than in controls (6.9 ± 0.7% vs 7.4 ± 0.9% of total intracranial volume, p<0.05). Significantly less grey matter density was found in multiple brain regions, both above and below the tentorium cerebelli, than in controls (p<0.05). 12 (36%) patients demonstrated WMAs unexpected for the patient's age, with the highest incidence occurring in the headache subgroup. This subgroup averaged almost twice the number of WMAs per MR imaging than the subgroup with balance disturbance and six times more than the subgroup with sensory loss. CONCLUSION: Patients with established coeliac disease referred for neurological opinion show significant brain abnormality on MR imaging. MR imaging may provide valuable biomarkers of disease in this patient cohort.