Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups.

BACKGROUND: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification 'gold-standard', typically delivered 3-4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). METHODS: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. FINDINGS: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4-8.1, p = 0.025). INTERPRETATION: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. FUNDING: Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.

Bevacizumab, Irinotecan, or Topotecan Added to Temozolomide for Children With Relapsed and Refractory Neuroblastoma: Results of the ITCC-SIOPEN BEACON-Neuroblastoma Trial.

PURPOSE: Outcomes for children with relapsed and refractory high-risk neuroblastoma (RR-HRNB) remain dismal. The BEACON Neuroblastoma trial (EudraCT 2012-000072-42) evaluated three backbone chemotherapy regimens and the addition of the antiangiogenic agent bevacizumab (B). MATERIALS AND METHODS: Patients age 1-21 years with RR-HRNB with adequate organ function and performance status were randomly assigned in a 3 × 2 factorial design to temozolomide (T), irinotecan-temozolomide (IT), or topotecan-temozolomide (TTo) with or without B. The primary end point was best overall response (complete or partial) rate (ORR) during the first six courses, by RECIST or International Neuroblastoma Response Criteria for patients with measurable or evaluable disease, respectively. Safety, progression-free survival (PFS), and overall survival (OS) time were secondary end points. RESULTS: One hundred sixty patients with RR-HRNB were included. For B random assignment (n = 160), the ORR was 26% (95% CI, 17 to 37) with B and 18% (95% CI, 10 to 28) without B (risk ratio [RR], 1.52 [95% CI, 0.83 to 2.77]; P = .17). Adjusted hazard ratio for PFS and OS were 0.89 (95% CI, 0.63 to 1.27) and 1.01 (95% CI, 0.70 to 1.45), respectively. For irinotecan ([I]; n = 121) and topotecan (n = 60) random assignments, RRs for ORR were 0.94 and 1.22, respectively. A potential interaction between I and B was identified. For patients in the bevacizumab-irinotecan-temozolomide (BIT) arm, the ORR was 23% (95% CI, 10 to 42), and the 1-year PFS estimate was 0.67 (95% CI, 0.47 to 0.80). CONCLUSION: The addition of B met protocol-defined success criteria for ORR and appeared to improve PFS. Within this phase II trial, BIT showed signals of antitumor activity with acceptable tolerability. Future trials will confirm these results in the chemoimmunotherapy era.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

OBJECTIVE: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS: 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION: The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Identifying new biomarkers of aggressive Group 3 and SHH medulloblastoma using 3D hydrogel models, single cell RNA sequencing and 3D OrbiSIMS imaging.

The most common malignant brain tumour in children, medulloblastoma (MB), is subdivided into four clinically relevant molecular subgroups, although targeted therapy options informed by understanding of different cellular features are lacking. Here, by comparing the most aggressive subgroup (Group 3) with the intermediate (SHH) subgroup, we identify crucial differences in tumour heterogeneity, including unique metabolism-driven subpopulations in Group 3 and matrix-producing subpopulations in SHH. To analyse tumour heterogeneity, we profiled individual tumour nodules at the cellular level in 3D MB hydrogel models, which recapitulate subgroup specific phenotypes, by single cell RNA sequencing (scRNAseq) and 3D OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) imaging. In addition to identifying known metabolites characteristic of MB, we observed intra- and internodular heterogeneity and identified subgroup-specific tumour subpopulations. We showed that extracellular matrix factors and adhesion pathways defined unique SHH subpopulations, and made up a distinct shell-like structure of sulphur-containing species, comprising a combination of small leucine-rich proteoglycans (SLRPs) including the collagen organiser lumican. In contrast, the Group 3 tumour model was characterized by multiple subpopulations with greatly enhanced oxidative phosphorylation and tricarboxylic acid (TCA) cycle activity. Extensive TCA cycle metabolite measurements revealed very high levels of succinate and fumarate with malate levels almost undetectable particularly in Group 3 tumour models. In patients, high fumarate levels (NMR spectroscopy) alongside activated stress response pathways and high Nuclear Factor Erythroid 2-Related Factor 2 (NRF2; gene expression analyses) were associated with poorer survival. Based on these findings we predicted and confirmed that NRF2 inhibition increased sensitivity to vincristine in a long-term 3D drug treatment assay of Group 3 MB. Thus, by combining scRNAseq and 3D OrbiSIMS in a relevant model system we were able to define MB subgroup heterogeneity at the single cell level and elucidate new druggable biomarkers for aggressive Group 3 and low-risk SHH MB.

MRI and Molecular Characterization of Pediatric High-Grade Midline Thalamic Gliomas: The HERBY Phase II Trial.

Background Diffuse midline gliomas (DMG) are characterized by a high incidence of H3 K27 mutations and poorer outcome. The HERBY trial has provided one of the largest cohorts of pediatric DMGs with available radiologic, histologic-genotypic, and survival data. Purpose To define MRI and molecular characteristics of DMG. Materials and Methods This study is a secondary analysis of a prospective trial (HERBY; ClinicalTrials.gov identifier, NCT01390948) undertaken between October 2011 and February 2016. Among 121 HERBY participants, 50 had midline nonpontine-based tumors. Midline high-grade gliomas were reclassified into DMG H3 K27 mutant, H3 wild type with enhancer of zest homologs inhibitory protein overexpression, epidermal growth factor receptormutant, or not otherwise stated. The epicenter of each tumor and other radiologic characteristics were ascertained from MRI and correlated with the new subtype classification, histopathologic characteristics, surgical extent, and outcome parameters. Kaplan-Meier curves and log-rank tests were applied to determine and describe survival differences between groups. Results There were 42 participants (mean age, 12 years ± 4 [SD]; 23 girls) with radiologically evaluable thalamic-based DMG. Eighteen had partial thalamic involvement (12 thalamopulvinar, six anteromedial), 10 involved a whole thalamus, nine had unithalamic tumors with diffuse contiguous extension, and five had bithalamic tumors (two symmetric, three partial). Twenty-eight participants had DMG H3 K27 mutant tumors; there were no differences in outcome compared with other DMGs (n = 4). Participants who underwent major debulking or total or near-total resection had longer overall survival (OS): 18.5 months vs 11.4 months (P = .02). Enrolled participants who developed leptomeningeal metastatic dissemination before starting treatment had worse outcomes (event-free survival, 2.9 months vs 8.0 months [P = .02]; OS, 11.4 months vs 18.5 months [P = .004]). Conclusion Thalamic involvement of diffuse midline gliomas ranged from localized partial thalamic to holo- or bithalamic with diffuse contiguous spread and had poor outcomes, irrespective of H3 K27 subtype alterations. Leptomeningeal dissemination and less than 50% surgical resection were adverse risk factors for survival. Clinical trial registration no. NCT01390948 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Widjaja in this issue.

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study.

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; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=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.

Metabolite selection for machine learning in childhood brain tumour classification.

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 1 H-MRS through support vector machine and 75% for 3 T 1 H-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.

The Molecular Landscape of Medulloblastoma in Teenagers and Young Adults.

Medulloblastoma (MB) is a childhood malignant brain tumour but also occurs in teenagers and young adults (TYA). Considering that MB is heterogeneous, this study aimed to define the molecular landscape of MBs in TYAs. We collated more than 2000 MB samples that included 287 TYA patients (13-24 years). We performed computational analyses consisting of genome-wide methylation and transcriptomic profiles and developed a prognostics model for the TYAs with MB. We identified that TYAs predominantly comprised of Group 4 (40%) and Sonic Hedgehog (SHH)-activated (33%) tumours, with Wingless-type (WNT, 17%) and Group 3 (10%) being less common. TYAs with SHH tumours displayed significantly more gene expression alterations, whereas no gene was detected in the Group 4 tumours. Across MB subgroups, we identified unique and shared sets of TYA-specific differentially methylated probes and DNA-binding motifs. Finally, a 22-gene signature stratified TYA patients into high- and low-risk groups, and the prognostic significance of these risk groups persisted in multivariable regression models (P = 0.001). This study is an important step toward delineating the molecular landscape of TYAs with MB. The emergence of novel genes and pathways may provide a basis for improved clinical management of TYA with MB.

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours.

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 < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.

Meta-Analysis of Apparent Diffusion Coefficient in Pediatric Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma.

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 < 0.05 was considered statistically significant, and a Hedge's g value of >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.

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.

The role of Magnetic Resonance Images (MRIs) in coping for patients with brain tumours and their parents: a qualitative study.

BACKGROUND: When children and young people (CYP) are diagnosed with a brain tumour, Magnetic Resonance Imaging (MRI) is key to the clinical management of this condition. This can produce hundreds, and often thousands, of Magnetic Resonance Images (MRIs). METHODS: Semi-structured interviews were undertaken with 14 families (15 parents and 8 patients), and analysed using Grounded Theory. Analysis was supported by the Framework Method. RESULTS: Although the focus of the research was whether paediatric patients and their families find viewing MRIs beneficial, all patients and parents discussed difficult times during the illness and using various strategies to cope. This article explores the identified coping strategies that involved MRIs, and the role that MRIs can play in coping. Coping strategies were classified under the aim of the strategy when used: 'Normalising'; 'Maintaining hope and a sense of the future'; 'Dealing with an uncertain future'; and 'Seeking Support'. CONCLUSIONS: Coping and finding ways to cope are clearly used by patients and their families and are something that they wish to discuss, as they were raised in conversations that were not necessarily about coping. This suggests clinicians should always allow time and space (in appointments, consultations, or impromptu conversations on the ward) for patient families to discuss ways of coping. MRIs were found to be used in various ways: to maintain or adapt normal; maintain hope and a sense of the future; deal with an uncertain future; and seek support from others. Clinicians should recognise the potential for MRIs to aid coping and if appropriate, suggest that families take copies of scans (MRIs) home. Professional coaches or counsellors may also find MRIs beneficial as a way to remind families that the child is in a more stable or 'better' place than they have been previously.

European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours.

INTRODUCTION: 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. MATERIALS AND METHOD: 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. RESULTS: 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. CONCLUSION: The SIOPE brain tumour imaging protocol will enable consistent imaging across multiple centres involved in paediatric CNS tumour studies.

Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation.

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.

Molecular characterization of DICER1-mutated pituitary blastoma.

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.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.

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 < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 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.

Clinical Outcomes and Complications of Pituitary Blastoma.

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.