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Purpose: Medulloblastoma is the most common malignant brain tumor occurring in childhood and is a significant cause of morbidity and mortality in pediatric oncology. More intense treatment strategies are recommended for patients displaying high-risk factors; however, considerable variation in outcome remains, indicating a need for improved predictive markers. In this study, 1H magnetic resonance spectroscopy (MRS) was used to investigate noninvasive molecular biomarkers of survival in medulloblastoma.Experimental Design: MRS was performed on a series of 35 biopsy-confirmed medulloblastoma cases. One case was excluded because of poor quality MRS. The prognostic value of MRS detectable biomarkers was investigated using Cox regression, retrospectively (N = 15). A subsequent validation analysis (N = 19) was also performed to reduce the chance of type I errors. Where available, high-resolution ex vivo MRS of biopsy tissue was used to confirm biomarker assignments.Results: The retrospective analysis revealed that creatine, glutamate, and glycine were markers of survival (P < 0.01). The validation analysis showed that glutamate was a robust marker, with a hazard ration (HR) of 8.0 for the full dataset (P = 0.0003, N = 34). A good correlation between in vivo and ex vivo MRS glutamate/total-choline was found (P = 0.001), validating the in vivo assignment. Ex vivo glutamate/total-choline was also associated with survival (P < 0.01).Conclusion: The identification of glutamate as a predictive biomarker of survival in pediatric medulloblastoma provides a clinically viable risk factor and highlights the importance of more detailed studies into the metabolism of this disease. Noninvasive biomarker detection using MRS may offer improved disease monitoring and potential for widespread use following multicenter validation. Clin Cancer Res; 20(17); 4532–9. ©2014 AACR.

Supplementary Methods (Text 1). A description of the treatment protocols used during the study period. Supplementary Figure 1. Example medulloblastoma fits where the lowest smooth black line is the baseline; the middle black line represents the acquired spectrum following pre-processing; the red line shows the spectral fit and the top black line shows the residual between the fit and acquired spectrum. A) shows a distorted baseline leading to qc failure whereas B) is an example of an ideal smooth baseline.

Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1High;CHD7Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1High;CHD7Low xenograft model., BMI1 and CHD7 are chromatin remodelling genes with a role in medulloblastoma pathogenesis. Here, the authors demonstrate that the BMI1High/CHD7Low signature mediates metabolic adaptation in G4 MB and predicts response to inositol treatment either alone or in combination with chemotherapy.

Pituitary blastoma (PitB) has recently been identified as a rare and potentially lethal pediatric intracranial tumor. All cases that have been studied molecularly possess at least one DICER1 pathogenic variant. Here, we characterized nine pituitary samples, including three fresh frozen PitBs, three normal fetal pituitary glands and three normal postnatal pituitary glands using small-RNA-Seq, RNA-Seq, methylation profiling, whole genome sequencing and Nanostring® miRNA analyses; an extended series of 21 pituitary samples was used for validation purposes. These analyses demonstrated that DICER1 RNase IIIb hotspot mutations in PitBs induced improper processing of miRNA precursors, resulting in aberrant 5p-derived miRNA products and a skewed distribution of miRNAs favoring mature 3p over 5p miRNAs. This led to dysregulation of hundreds of 5p and 3p miRNAs and concomitant dysregulation of numerous mRNA targets. Gene expression analysis revealed PRAME as the most significantly upregulated gene (500-fold increase). PRAME is a member of the Retinoic Acid Receptor (RAR) signaling pathway and in PitBs, the RAR, WNT and NOTCH pathways are dysregulated. Cancer Hallmarks analysis showed that PI3K pathway is activated in the tumors. Whole genome sequencing demonstrated a quiet genome with very few somatic alterations. The comparison of methylation profiles to publicly available data from ~ 3000 other central nervous system tumors revealed that PitBs have a distinct methylation profile compared to all other tumors, including pituitary adenomas. In conclusion, this comprehensive characterization of DICER1-related PitB revealed key molecular underpinnings of PitB and identified pathways that could potentially be exploited in the treatment of this tumor.

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P −3 mm2 s−1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

BACKGROUND Medulloblastoma, ependymoma, and pilocytic astrocytoma are common pediatric posterior fossa tumors. These tumors show overlapping characteristics on conventional MRI scans, making diagnosis difficult. PURPOSE To investigate whether apparent diffusion coefficient (ADC) values differ between tumor types and to identify optimum cut-off values to accurately classify the tumors using different performance metrics. STUDY TYPE Systematic review and meta-analysis. SUBJECTS Seven studies reporting ADC in pediatric posterior fossa tumors (115 medulloblastoma, 68 ependymoma, and 86 pilocytic astrocytoma) were included following PubMed and ScienceDirect searches. SEQUENCE AND FIELD STRENGTH Diffusion weighted imaging (DWI) was performed on 1.5 and 3 T across multiple institution and vendors. ASSESSMENT The combined mean and standard deviation of ADC were calculated for each tumor type using a random-effects model, and the effect size was calculated using Hedge's g. STATISTICAL TESTS Sensitivity/specificity, weighted classification accuracy, balanced classification accuracy. A P value 1.2 was considered to represent a large difference. RESULTS The mean (± standard deviation) ADCs of medulloblastoma, ependymoma, and pilocytic astrocytoma were 0.76 ± 0.16, 1.10 ± 0.10, and 1.49 ± 0.16 mm2 /sec × 10-3 . To maximize sensitivity and specificity using the mean ADC, the cut-off was found to be 0.96 mm2 /sec × 10-3 for medulloblastoma and ependymoma and 1.26 mm2 /sec × 10-3 for ependymoma and pilocytic astrocytoma. The meta-analysis showed significantly different ADC distributions for the three posterior fossa tumors. The cut-off values changed markedly (up to 7%) based on the performance metric used and the prevalence of the tumor types. DATA CONCLUSION There were significant differences in ADC between tumor types. However, it should be noted that only summary statistics from each study were analyzed and there were differences in how regions of interest were defined between studies. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 3.

Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.

INTRODUCTION: Perfusion is associated with grade and survival in children’s brain tumours. Dynamic susceptibility-contrast (DSC-) MRI measures perfusion non-invasively, estimating relative cerebral blood volume (rCBV). We previously showed significant differences between pre-treatment rCBV in low- and high-grade tumours in a multicentre study. Contrast agent leakage from tumour vessels during acquisition affects rCBV accuracy. A contrast agent pre-bolus can be given but this can be challenging in a clinical environment, introducing variability. Alternatively, a single bolus can be administered with leakage correction applied when processing the data. We investigated pre-treatment rCBV values in a multicentre study without pre-bolus administration. METHODS: Thirty-six patients underwent pre-treatment DSC-MRI scans at 2 centres on 4 different scanners. Protocols were variable. Pixel-by-pixel contrast agent concentration time courses were analysed. Maps of uncorrected (rCBVuncorr) and leakage-corrected rCBV (rCBVcorr) were produced. Whole-tumour regions-of-interest were defined and median whole-tumour DSC-MRI parameters calculated. Patients subsequently underwent surgery / biopsy. Tumours were classified and graded. RESULTS: Twelve tumours were classified as low-grade; 24 as high-grade. Median whole-tumour rCBVuncorr was significantly higher in high-grade tumours than in low-grade tumours (1.628 vs -0.167, p

Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.

Background 1H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. Methods Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. Results Radiologists’ principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. Conclusions MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

Proton Magnetic Resonance Spectroscopy ((1)H MRS) provides non-invasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Whilst most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges towards obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the popular point resolved spectroscopy (PRESS) localization sequence was found to be unacceptably high at 3T, and the use of the semi-adiabatic localization by adiabatic selective refocusing (semi-LASER) sequence is a recommended solution. The incorporation of simulated metabolite basis-sets into analysis routines is recommended for reliably capturing the full spectral detail available from short echo time acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B(0)) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. We anticipate the implementation of these recommendations will strengthen current clinical applications and advance progress towards developing and validating new MRS biomarkers for clinical use.

Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

Standardisation of imaging acquisition is essential in facilitating multicentre studies related to childhood CNS tumours. It is important to ensure that the imaging protocol can be adopted by centres with varying imaging capabilities without compromising image quality. An imaging protocol has been developed by the Brain Tumour Imaging Working Group of the European Society for Paediatric Oncology (SIOPE) based on consensus among its members, which consists of neuroradiologists, imaging scientists and paediatric neuro-oncologists. This protocol has been developed to facilitate SIOPE led studies and regularly reviewed by the imaging working group. The protocol consists of essential MRI sequences with imaging parameters for 1.5 and 3 Tesla MRI scanners and a set of optional sequences that can be used in appropriate clinical settings. The protocol also provides guidelines for early post-operative imaging and surveillance imaging. The complementary use of multimodal advanced MRI including diffusion tensor imaging (DTI), MR spectroscopy and perfusion imaging is encouraged, and optional guidance is provided in this publication. The SIOPE brain tumour imaging protocol will enable consistent imaging across multiple centres involved in paediatric CNS tumour studies.

Background Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. Objective We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. Materials and methods Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. Results Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. –0.14±5.55; PP=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. Conclusion Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children’s brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.

Context Pituitary blastoma is a rare, dysontogenetic hypophyseal tumor of infancy first described in 2008, strongly suggestive of DICER1 syndrome. Objective This work aims to describe genetic alterations, clinical courses, outcomes, and complications in all known pituitary blastoma cases. Design and Setting A multi-institutional case series is presented from tertiary pediatric oncology centers. Patients Patients included children with pituitary blastoma. Interventions Genetic testing, surgery, oncologic therapy, endocrine support are reported. Outcome Measures Outcome measures included survival, long-term morbidities, and germline and tumor DICER1 genotypes. Results Seventeen pituitary blastoma cases were studied (10 girls and 7 boys); median age at diagnosis was 11 months (range, 2-24 months). Cushing syndrome was the most frequent presentation (n = 10). Cushingoid stigmata were absent in 7 children (2 with increased adrenocorticotropin [ACTH]; 5 with normal/unmeasured ACTH). Ophthalmoplegia and increased intracranial pressure were also observed. Surgical procedures included gross/near-total resection (n = 7), subtotal resection (n = 9), and biopsy (n = 1). Six children received adjuvant therapy. At a median follow-up of 6.7 years, 9 patients were alive; 8 patients died of the following causes: early medical/surgical complications (n = 3), sepsis (n = 1), catheter-related complication (n = 1), aneurysmal bleeding (n = 1), second brain tumor (n = 1), and progression (n = 1). Surgery was the only intervention for 5 of 9 survivors. Extent of resection, but neither Ki67 labeling index nor adjuvant therapy, was significantly associated with survival. Chronic complications included neuroendocrine (n = 8), visual (n = 4), and neurodevelopmental (n = 3) deficits. Sixteen pituitary blastomas were attributed to DICER1 abnormalities. Conclusions Pituitary blastoma is a locally destructive tumor associated with high mortality. Surgical resection alone provides long-term disease control for some patients. Quality survival is possible with long-term neuroendocrine management.

This article aims to explore and record the role of shadowing in preparation for a qualitative study involving children and families with sensitive health issues. The researcher was engaged for a study involving qualitative research involving paediatric patients (those under 18 years old) and their families, but was unfamiliar with a hospital environment and interviewing children and young people (CYP) with a serious health condition. The researcher &lsquo, shadowed&rsquo, healthcare professionals (HCPs) at a children&rsquo, s hospital during their day-to-day work in order to prepare for the research interviewing. From shadowing, the researcher gained: familiarity with a hospital environment, organisational processes, and medical terminology, an understanding of the appropriate ways to refer to patients, confidence and competence in talking to children with serious health conditions, and resilience to becoming upset during interviews while hearing patients&rsquo, distressing stories &ndash, they became &lsquo, desensitised&rsquo, Shadowing can therefore be highly beneficial for researchers undertaking research in unfamiliar contexts, environments, and populations prior to interviewing.

BACKGROUND: (1)H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. METHODS: Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. RESULTS: Radiologists’ principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. CONCLUSIONS: MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

Introduction Brain tumours are a common cause of death in the paediatric population. We have previously shown that MR imaging and spectroscopy can be used to non-invasively differentiate between tumour types. Here, we demonstrate that functional imaging can be highly predictive of survival and grade in a paediatric cohort. Methods Perfusion (PWI) and diffusion weighted imaging (DWI) were performed in a multi-site (Birmingham Children’s Hospital, Royal Victoria Infirmary, Alder Hey, Nottingham) cohort ([grade, 5-year survival alive:dead number] = [I,15:1],[II, 5:1],[III,2:3],[IV,8:11]). ROIs were drawn on T2 imaging and functional imaging features (mean, standard deviation, skewness, and kurtosis) were derived. Supervised machine learning was used to predict 5-year survival and tumour grade from features. ANOVA and post-hoc tests were used to assess differences in features between grade and 5-year survival status. Results 5-year survival was predicted with 89%, 85%, and 87% accuracy with all imaging, perfusion, or diffusion features, respectively. A significant difference in perfusion was found between surviving and diseased participants (1.71 ± 0.82 vs 2.62 ± 1 mL/100g/min, respectively, p < 0.05). A significant difference in ADC (mm2 s-1) between tumour grades was found (1 vs 4 (1533 ± 458 vs 857 ± 239), 4 vs 3 (857 ± 239 vs 1197 ± 137), 4 vs 2 (857 ± 239 vs 1440 ± 557), corrected p < 0.05). Conclusion We have shown that perfusion and diffusion imaging features can be used to non-invasively assess tumour grade and estimate 5-year survival status in a cohort of paediatric brain tumours.

BACKGROUND Metabolite concentrations are fundamental biomarkers of disease and prognosis. Magnetic resonance spectroscopy (MRS) is a noninvasive method for measuring metabolite concentrations; however, quantitation is affected by T2 relaxation. PURPOSE To estimate T2 relaxation times in pediatric brain tumors and assess how variation in T2 relaxation affects metabolite quantification. STUDY TYPE Retrospective. POPULATION Twenty-seven pediatric brain tumor patients (n = 17 pilocytic astrocytoma and n = 10 medulloblastoma) and 24 age-matched normal controls. FIELD STRENGTH/SEQUENCE Short- (30 msec) and long-echo (135 msec) single-voxel MRS acquired at 1.5T. ASSESSMENT T2 relaxation times were estimated by fitting signal amplitudes at two echo times to a monoexponential decay function and were used to correct metabolite concentration estimates for relaxation effects. STATISTICAL TESTS One-way analysis of variance (ANOVA) on ranks were used to analyze the mean T2 relaxation times and metabolite concentrations for each tissue group and paired Mann-Whitney U-tests were performed. RESULTS The mean T2 relaxation of water was measured as 181 msec, 123 msec, 90 msec, and 86 msec in pilocytic astrocytomas, medulloblastomas, basal ganglia, and white matter, respectively. The T2 of water was significantly longer in both tumor groups than normal brain (P

The rare pediatric embryonal tumors retinoblastoma, medulloblastoma and neuroblastoma derive from neuroectodermal tissue and share similar histopathological features despite different anatomical locations and diverse clinical outcomes. As metabolism can reflect genetic and histological features, we investigated whether the metabolism of embryonal tumors reflects their similar histology, shared developmental and neural origins, or tumor location. We undertook metabolic profiling on 50 retinoblastoma, 39 medulloblastoma and 70 neuroblastoma using high resolution magic angle spinning magnetic resonance spectroscopy (1H-MRS). Mean metabolite concentrations identified several metabolites that were significantly different between the tumor groups including taurine, hypotaurine, glutamate, glutamine, GABA, phosphocholine, N-acetylaspartate, creatine, glycine and myoinositol, p < 0.0017. Unsupervised multivariate analysis found that each tumor group clustered separately, with a unique metabolic profile, influenced by their underlying clinical diversity. Taurine was notably high in all tumors consistent with prior evidence from embryonal tumors. Retinoblastoma and medulloblastoma were more metabolically similar, sharing features associated with the central nervous system (CNS). Neuroblastoma had features consistent with neural tissue, but also contained significantly higher myoinositol and altered glutamate-glutamine ratio, suggestive of differences in the underlying metabolism of embryonal tumors located outside of the CNS. Despite the histological similarities and shared neural metabolic features, we show that individual neuroectodermal derived embryonal tumors can be distinguished by tissue metabolic profile. Pathway analysis suggests the alanine-aspartate-glutamate and taurine-hypotaurine metabolic pathways may be the most pertinent pathways to investigate for novel therapeutic strategies. This work strengthens our understanding of the biology and metabolic pathways underlying neuroectodermal derived embryonal tumors of childhood.

Intracranial germ cell tumors are a heterogeneous group of tumors, broadly classified into germinomatous, nongerminomatous, or teratoma subtypes. Treatment has evolved over recent decades to include multimodal therapy combining surgery, radiotherapy, and chemotherapy. Although the majority of intracranial germs cell tumors are treated successfully, management can be fraught with complexities and present significant clinical challenges. Bifocal disease is well described, but rare, and therefore its behavior is not well characterized, particularly in nongerminomatous disease. This case report presents an interesting case, with both rare and common complications, in particular, to emphasize the challenges of germ cell tumor management. With a focus on bifocal disease, we review the published cases and highlight how advanced imaging and magnetic resonance spectroscopy can be used in management. Advances in biology, targeted agents, and novel diagnostic tools are also discussed.

Context Brain white matter hyperintensities are seen on routine clinical imaging in 46% of adults with congenital adrenal hyperplasia (CAH). The extent and functional relevance of these abnormalities have not been studied with quantitative magnetic resonance imaging (MRI) analysis. Objective To examine white matter microstructure, neural volumes, and central nervous system (CNS) metabolites in CAH due to 21-hydroxylase deficiency (21OHD) and to determine whether identified abnormalities are associated with cognition, glucocorticoid, and androgen exposure. Design, Setting, and Participants A cross-sectional study at a tertiary hospital including 19 women (18 to 50 years) with 21OHD and 19 age-matched healthy women. Main Outcome Measure Recruits underwent cognitive assessment and brain imaging, including diffusion weighted imaging of white matter, T1-weighted volumetry, and magnetic resonance spectroscopy for neural metabolites. We evaluated white matter microstructure by using tract-based spatial statistics. We compared cognitive scores, neural volumes, and metabolites between groups and relationships between glucocorticoid exposure, MRI, and neurologic outcomes. Results Patients with 21OHD had widespread reductions in white matter structural integrity, reduced volumes of right hippocampus, bilateral thalami, cerebellum, and brainstem, and reduced mesial temporal lobe total choline content. Working memory, processing speed, and digit span and matrix reasoning scores were reduced in patients with 21OHD, despite similar education and intelligence to controls. Patients with 21OHD exposed to higher glucocorticoid doses had greater abnormalities in white matter microstructure and cognitive performance. Conclusion We demonstrate that 21OHD and current glucocorticoid replacement regimens have a profound impact on brain morphology and function. If reversible, these CNS markers are a potential target for treatment., Using quantitative neuroimaging and cognitive assessment, we demonstrate that 21OHD and current glucocorticoid replacement regimens have a profound impact on brain morphology and function.

AIM To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma. MATERIALS AND METHODS A retrospective analysis was undertaken of 76 patients (46 tPD, 30 psPD) with early enhancing disease following chemoradiotherapy for glioblastoma. Outcome was determined on follow-up until 6 months post-chemoradiotherapy. Models comprised clinical characteristics, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and 307 quantitative imaging features extracted from enhancing disease and perilesional oedema masks on early post-chemoradiotherapy contrast-enhanced T1-weighted imaging, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm. Naive Bayes five-fold cross-validation was used to validate the final model. RESULTS Top selected features included age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask, three radiomic features from the enhancing disease mask on ADC, and one radiomic feature from the perilesional oedema mask on T2WI. The final model had an area under the receiver operating characteristics curve (AUC) of 0.80, sensitivity 78.2%, specificity 66.7%, and accuracy of 73.7%. CONCLUSION Incorporating a machine learning-based approach using quantitative radiomic features from standard-of-care magnetic resonance imaging (MRI), in combination with clinical characteristics and MGMT promoter methylation status has a complementary effect and improves model performance for early prediction of glioblastoma treatment response.

BACKGROUND Treatment of medulloblastoma has evolved substantially with more chemotherapy, risk-adapted dosing of radiotherapy (RT) and new RT techniques. We present the endocrine profile for our patients treated over a 20-year period. METHODS The charts of patients treated for medulloblastoma between 1/1/00 and 31/12/19 were reviewed. 105 were available. Group 1 received chemotherapy alone, Group 2 received 23.4 Gy whole CNS RT with a posterior fossa (PF) boost to 54 Gy, Group 3 received > 35 Gy whole CNS RT with PF boost to 54–59 Gy, Group 4 received PF RT to 54 Gy. All received chemotherapy according to national guidelines or clinical trials relevant at the time. RESULTS Group 1 (M:F 11:6, 7 survivors mean age 2 years range 1–7) had no endocrinopathies. At 5 years from diagnosis Group 2 (M:F 15:13) and Group 3 (M:F 35:14) had the following % RESULTS Survival 77:61; Growth Hormone deficiency 92:100; Thyroid deficiency 75:81; ACTH deficiency 42:33. Girls were more likely to need sex hormone replacement than boys. Group 4 (M:F 7:5 mean age 2) were all treated in the first decade. 3 survivors, one GH deficiency, one thyroxine deficiency, one both. CONCLUSIONS There is a trend to earlier endocrinopathies in the group 3 vs group 2 patients, but it does not reach statistical significance. Girls are more likely to need sex hormone replacement than boys. This investigation provides a contemporary profile of endocrinopathy after treatment for medulloblastoma that can be used for future comparisons.

Medulloblastoma (MB) is the most common paediatric malignant brain tumour and is classified into four distinct molecular subgroups (WNT, SHH, G3 and G4), each of them further subdivided into subtypes with different prognosis and responses to therapy. Deregulation of chromatin modifier genes plays an essential role in MB, particularly in the G4 subgroup, the least understood of all subgroups, despite being the most common and associated with poor prognosis. A BMI1High; CHD7Low molecular signature identifies patients with poor survival within this subgroup. We show that BMI1High; CHD7Low mediates a novel epigenetic regulation of inositol metabolism in both G4 MB cells and patients. These tumours display hyperactivation of the AKT/mTOR pathway which leads to energetic rewiring characterized by enhanced glycolytic capacity and reduced mitochondrial function. We demonstrate that inositol administration counteracts this metabolic alteration, impairs MB proliferation in vitro and significantly extends survival in an in vivo pre-clinical model. Moreover, inositol synergises with cisplatin, a chemotherapy agent currently used in MB treatment, enhancing its therapeutic effect in vivo. Importantly, cerebellar neural stem cells bearing the BMI1High; CHD7Low signature do not show metabolic adaptation and are thus resistant to inositol treatment, highlighting a fundamental difference between normal and neoplastic metabolism in the developing cerebellum. In summary, we have identified an actionable vulnerability in a pre-clinical setting modelling a molecularly defined group of MB patients, the translational value of which can now be explored in signature-matched clinical trials in MB.

Background Magnetic resonance spectroscopy (MRS) aids noninvasive diagnosis of pediatric brain tumors, but use in clinical practice is not well documented. We aimed to review clinical use of MRS, establish added value in noninvasive diagnosis, and investigate potential impact on patient care. Methods Sixty-nine children with lesions imaged using MRS and reviewed by the tumor board from 2014 to 2016 met inclusion criteria. Contemporaneous MRI diagnosis, spectroscopy analysis, histopathology, and clinical information were reviewed. Final diagnosis was agreed on by the tumor board at study end. Results Five cases were excluded for lack of documented MRI diagnosis. The principal MRI diagnosis by pediatric radiologists was correct in 59%, increasing to 73% with addition of MRS. Of the 73%, 19.1% (95% CI, 9.1%-33.3%) were incorrectly diagnosed with MRI alone. MRS led to a significant improvement in correct diagnosis over all tumor types (P = .012). Of diagnoses correctly made with MRI, confidence increased by 37% when adding MRS, with no patients incorrectly re-diagnosed. Indolent lesions were diagnosed noninvasively in 85% of cases, with MRS a major contributor to 91% of these diagnoses. Of all patients, 39% were managed without histopathological diagnosis. MRS contributed to diagnosis in 68% of this group, modifying it in 12%. MRS influenced management in 33% of cases, mainly through avoiding and guiding biopsy and aiding tumor characterization. Conclusion MRS can improve accuracy and confidence in noninvasive diagnosis of pediatric brain lesions in clinical practice. There is potential to improve outcomes through avoiding biopsy of indolent lesions, aiding tumor characterization, and facilitating earlier family discussions and treatment planning.

OBJECTIVE: To investigate correlations between MRI perfusion metrics measured by dynamic susceptibility contrast and arterial spin labelling in paediatric brain tumours. METHODS: 15 paediatric patients with brain tumours were scanned prospectively using pseudo-continuous arterial spin labelling (ASL) and dynamic susceptibility contrast (DSC-) MRI with a pre-bolus to minimise contrast agent leakage. Cerebral blood flow (CBF) maps were produced using ASL. Cerebral blood volume (CBV) maps with and without contrast agent leakage correction using the Boxerman technique and the leakage parameter, K(2), were produced from the DSC data. Correlations between the metrics produced were investigated. RESULTS: Histology resulted in the following diagnoses: pilocytic astrocytoma (n = 7), glioblastoma (n = 1), medulloblastoma (n = 1), rosette-forming glioneuronal tumour of fourth ventricle (n = 1), atypical choroid plexus papilloma (n = 1) and pilomyxoid astrocytoma (n = 1). Three patients had a non-invasive diagnosis of low-grade glioma. DSC CBV maps of T(1)-enhancing tumours were difficult to interpret without the leakage correction. CBV values obtained with and without leakage correction were significantly different (p < 0.01). A significant positive correlation was observed between ASL CBF and DSC CBV (r = 0.516, p = 0.049) which became stronger when leakage correction was applied (r = 0.728, p = 0.002). K(2) values were variable across the group (mean = 0.35, range = −0.49 to 0.64). CONCLUSION: CBV values from DSC obtained with and without leakage correction were significantly different. Large increases in CBV were observed following leakage correction in highly T(1)-enhancing tumours. DSC and ASL perfusion metrics were found to correlate significantly in a range of paediatric brain tumours. A stronger relationship between DSC and ASL was seen when leakage correction was applied to the DSC data. Leakage correction should be applied when analysing DSC data in enhancing paediatric brain tumours. ADVANCES IN KNOWLEDGE: We have shown that leakage correction should be applied when investigating enhancing paediatric brain tumours using DSC-MRI. A stronger correlation was found between CBF derived from ASL and CBV derived from DSC when a leakage correction was employed.

SHH medulloblastoma patients have a variable prognosis. Infants (

INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion.

BACKGROUND: Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in children with high-grade central nervous system (CNS) tumors, although no consensus has been reached regarding its effectiveness and whether earlier detection is associated with improved patient outcomes. This review aimed to evaluate this practice and any associated benefits and harms. METHODS: Systematic searches for relevant studies were undertaken in a number of databases, including MEDLINE and EMBASE, from 1985 to August 2018. Study selection and data extraction was undertaken independently by two reviewers. Due to heterogeneity between studies, no pooling of data was undertaken. Reporting followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: No comparative studies were identified. Three retrospective observational studies involving 306 patients were reviewed. All had high risk of bias by virtue of study design. Two studies reported outcomes by symptomatic status-both recurrence rates and overall survival for asymptomatic patients were comparable with those for clinically symptomatic patients. No quality-of-life outcomes were reported. CONCLUSION: There is a paucity of evidence to guide clinical practice as to the effectiveness of MRI surveillance in pediatric patients with high-grade CNS tumors. These studies do not clearly demonstrate benefit or harm for the practice. With more research needed, there is a role for researchers to build into future trials data collection on surveillance imaging to give more information for the assessment of imaging frequency and duration in asymptomatic patients. This is an important question not only to clinicians and patients and their families but also from a health service resource perspective.

Studies in adults have shown that brain tumour perfusion correlates with grade. These studies are dominated by gliomas grade II to IV which are rare in children. The standard method, Dynamic Susceptibility Contrast MRI, provides estimates of relative cerebral blood volume (rCBV) but contrast agent leakage affects rCBV accuracy. The majority of perfusion studies have been conducted at single centres and variation in acquisition protocols makes the generalizability of results questionable. The aim of this study was to compare leakage-corrected rCBV with grade in paediatric brain tumours at multiple centres. Scans were analysed from 85 patients at 4 centres on 6 scanners prior to treatment. MRI protocols varied between centres. Histological diagnoses including grade were obtained. Whole-tumour median rCBV was significantly higher in the 45 high grade than the 40 low grade tumours (2.54 ± 1.63 ml/100ml vs 1.68 ± 1.36 ml/100ml, p=0.010). Low grade tumours, particularly pilocytic astrocytomas (grade I), displayed more contrast agent leakage consistent with their appearance on contrast enhanced images and required more leakage correction than high grade tumours. This finding differs from that in adults where contrast agent uptake is usually associated with higher grade. A cut-off of 1.70 ml/100ml for rCBV gave sensitivity and specificity of 76% and 65% respectively for discriminating grade. In summary, perfusion MRI can be used to help distinguish between low and high grade paediatric brain tumours. This finding is robust across multiple centres and acquisition protocols but correction should be made for leakage of contrast agent from the vessels.

OBJECTIVE: Paediatric low-grade glioma can progress or recur following primary surgical/non-surgical treatment and require further therapy. Studies have identified clinical characteristics predictive of disease progression. This study aims to determine whether size of residual disease following primary treatment is a prognostic indicator for predicting progression. METHODS: A retrospective analysis of 61 children with histopathologically confirmed low-grade glioma treated at Birmingham Children’s Hospital between 2004 and 2015 was performed. Residual tumour size following primary treatment was measured from MR imaging and analysed in context of clinical characteristics. RESULTS: 47.5% (29/61) children progressed during the follow-up period. A residual tumour size of ≤1cm(3) was predictive of better progression free survival for the entire cohort (p2cm(3)) progressed earlier (minimum 0.36 years) than those with smaller residuals (

Medulloblastoma (MB) is the most common malignant brain tumour arising in paediatric patients. These young patients are typically subjected to harsh regimens of treatments such as chemotherapy and radiotherapy, with varying degrees of success. The stratification of patients has been steadily improving, where patient tumours can be divided into four main groups (WNT, SHH, group 3 and group 4), each having differing prognostic implications, but does not accurately predict response to treatment. Previous work on this cancer, through the use of magnetic resonance spectroscopy (MRS), has uncovered a link between high glutamate concentrations and poor patient survival. Cellular metabolism is now accepted as a hallmark of cancer biology, where tumours are able to reprogram aspects of their metabolism to aid the biosynthetic, bioenergetic and redox burdens associated with increased growth and treatment resistance. We have shown that the medulloblastoma cell lines (ONS-76, DAOY and UW228.3) are addicted to glutamine, which is a precursor metabolite to glutamate. Furthermore, we have elucidated the importance of glutamate in treatment resistance and show the significance of glutathione (GSH) synthesis in this disease model. GSH is a tripeptide consisting of glutamate, glycine and cysteine. Therefore, glutamate has a potent role in the production of GSH, where it can be directly and indirectly utilised. Our research, through RNA inhibition of glutaminase 1 and 2, and the chemical inhibition of the xCT transporter, has pinpointed this metabolism as a potential therapeutic target and a means of sensitising MB cells to chemotherapy and irradiation.

Metabolic profiling by Magnetic Resonance Spectroscopy has shown the three most common paediatric cerebellar tumours are metabolically distinct; however, these techniques are limited by their sensitivity. Mass spectrometry is more sensitive and able to detect many more metabolites. In this study, metabolic phenotyping was performed to investigate the water-soluble metabolome and the lipidome of the three most common paediatric cerebellar tumours with the aim of informing a detailed metabolic pathway analysis. A discovery based experiment was performed on 60 surgically resected snap frozen pilocytic astrocytomas, ependymomas and medulloblastomas using UPLC-MS assays to detect water soluble and lipid metabolites. Principal component analysis demonstrates separation of the tumours based on the relative concentration of thousands of detected metabolites. The relative concentrations of glucose and glycolytic intermediates varied between the tumour types, as did metabolites involved in purine and pyrimidine metabolism. Key metabolites within the taurine metabolism pathway were found to be higher in medulloblastoma. Alterations were observed in lipid metabolism, with a wide range of relative concentration differences in di- and triacylglycerides, sphingolipids, ceramides, cardiolipins and glycerophospholipids. This study has shown that these tumours are metabolically distinct and offer the opportunities for stratified diagnosis and treatment. Some of the observed changes in relative concentration between the tumours reflect findings from spectroscopic studies, particularly with regards to taurine which is known to be higher in medulloblastoma. The ability of mass spectrometry to identify and measure lipid species and low concentration metabolites from tissue could allow targetable metabolic differences to be characterised and exploited.

BACKGROUND: (1)H-Magnetic Resonance Spectroscopy (MRS) facilitates non-invasive diagnosis of pediatric brain tumors through providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional Magnetic Resonance Imaging (MRI) are lacking. AIM: To evaluate diagnostic accuracy of qualitative radiological review of MRS for childhood brain tumors, and determine added clinical value compared with conventional MRI. METHODS: Children presenting to a Tertiary Pediatric Centre with brain lesions from December 2015-17 were eligible for inclusion. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by three radiologists, blind to histopathology. Proportions of correct diagnoses and inter-rater agreement at each stage were compared. Cases were reviewed to determine added value through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the Tumor Board (TB) at study end. RESULTS: Radiologists’ principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI+MRS resulted in significantly more additional correct diagnoses than MRI alone (p=0.035). There was significant increase in inter-rater agreement when correct with MRS (p=0.046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. CONCLUSION: MRS can improve accuracy of non-invasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has potential to facilitate early diagnosis, guide treatment planning and improve patient care.

Objective To explore the small-world properties of brain functional networks in patients with obstructive sleep apnea−hypopnea syndrome (OSAHS) to aid diagnosis. Methods A total of 29 OSAHS patients and 26 matched healthy volunteers were scanned with blood oxygen level−dependent functional magnetic resonance imaging (BOLD-fMRI) separately, and the whole brain was divided into 90 districts via automated anatomical labeling. The matrix Z was then built through a Fisher Z transformation. Two-sample t tests were applied to evaluate the changes in small-world properties in OSAHS patients compared to the control group. The properties included Eglobal, Elocal, and small-world parameters Lp, Cp, γ, λ, and σ. Results Both groups satisfied the small-world properties (σ > 1) within the sparsity range of 0.1–0.2. However, compared with the control group, the OSAHS group performed significantly lower in Cp, Elocal, and Eglobal (p Conclusion Both healthy and OSAHS patients exhibited small-world properties in functional networks, but a subset of these small-world properties in OSAHS patients performed differently. These changes will not only provide a new perspective for pathophysiological mechanisms of OSAHS but will also help in understanding the disease in terms of whole-brain functional networks.