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 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.

Comparison of intraoperative and post-operative 3-T MRI performed at 24-72 h following brain tumour resection in children.

PURPOSE: Intraoperative MRI (ioMRI) is a valuable tool aiding paediatric brain tumour resection. There is no published evidence comparing the effectiveness of the final intraoperative MRI and early post-operative (24-72 h) MRI as baseline scans following brain tumour resection. We aimed to evaluate whether the final ioMRI scan could serve as the post-operative baseline scan after paediatric brain tumour resections. METHODS: This prospective study compared the final ioMRI scan with the immediate post-operative MRI scan performed 24-72 h post-surgery. We included 20 patients aged 6.6-21 years undergoing brain tumour resection using ioMRI and were suitable for MRI scan without general anaesthesia. The scans were independently evaluated by experienced local and external paediatric neuroradiologists. Identical sequences in the final ioMRI and the 24-72-h MRI were compared to assess the extent of resection, imaging characteristics of residual tumour, the surgical field, extent of surgically induced contrast enhancement, and diffusion abnormalities. RESULTS: In 20 patients undergoing intraoperative and early post-operative MRI, there was no difference between ioMRI and 24-72-h post-op scans in identifying residual tumour. Surgically induced contrast enhancement was similar in both groups. There were more abnormalities on diffusion imaging and a greater degree of oedema around the surgical cavity on the 24-72-h scan. CONCLUSION: The final 3-T ioMRI scan may be used as a baseline post-operative scan provided standard imaging guidelines are followed and is evaluated jointly by the operating neurosurgeon and neuroradiologist. Advantages of final ioMRI as a baseline scan are identified.

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.

Response assessment in paediatric high-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group.

Response criteria for paediatric high-grade glioma vary historically and across different cooperative groups. The Response Assessment in Neuro-Oncology working group developed response criteria for adult high-grade glioma, but these were not created to meet the unique challenges in children with the disease. The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric high-grade glioma. Current practice and literature were reviewed to identify major challenges in assessing the response of paediatric high-grade gliomas to various treatments. For areas in which scientific investigation was scarce, consensus was reached through an iterative process. RAPNO response assessment recommendations include the use of MRI of the brain and the spine, assessment of clinical status, and the use of corticosteroids or antiangiogenics. Imaging standards for brain and spine are defined. Compared with the recommendations for the management of adult high-grade glioma, for paediatrics there is inclusion of diffusion-weighted imaging and a higher reliance on T2-weighted fluid-attenuated inversion recovery. Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.

Switching from linear to macrocyclic gadolinium-based contrast agents halts the relative T1 -Weighted signal increase in deep gray matter of children with brain tumors: A retrospective study.

BACKGROUND: Studies have shown signal intensity (SI) changes in the brains of children exposed to repeated doses of a gadolinium-based contrast agent (GBCA). HYPOTHESIS: The trajectory of changes in relative dentate nucleus (DN) and globus pallidus (GP) SI in children receiving multiple doses of GBCA will alter when switched from linear to macrocyclic agents. STUDY TYPE: Retrospective longitudinal. POPULATION: Thirty-five children, age range 0.5-17.0 years, undergoing brain tumor follow-up between 2006 and 2017. FIELD STRENGTH/SEQUENCE: Unenhanced T1 WI, serial scans at both 1.5T and 3T. ASSESSMENT: Regions of interest were drawn on DN, GP, and SIs normalized to middle cerebellar peduncle (DN/MCP) and cerebral white matter (GP/CWM), respectively. A change in SI ratios as a function of dose (slope gradient) calculated according to the type of contrast agent received: linear only, macrocyclic only, or switchover from linear to macrocyclic. For the latter, gradients were compared before and after switchover. The effect of anticancer treatment on slope gradient was tested. STATISTICAL TESTS: One-sample t-test or Mann-Whitney U-test for slope gradients differing from zero. Independent samples t-tests to compare slope gradient groups. Paired sample t-tests to compare slope gradients before and after switchover. RESULTS: A significant (P < 0.05) increase in SI ratio was observed following multiple doses of linear but not macrocyclic agents: mean percentage increase per dose in SI was 0.063% vs. -0.034% for DN/MCP, and 0.078% vs. 0.004% for GP/CWM ratios. A significant (P < 0.05) change of SI trajectory in the DN/MCP ratio was demonstrated when switching from a linear to macrocyclic agent. There was no difference in SI trajectory between patients who had anticancer therapies and those who did not, DN/MCP P = 0.740; GP/BWM P = 0.694. DATA CONCLUSION: Switching from linear to macrocyclic gadolinium-based contrast agents seems to halt the relative T1 signal increase in deep gray matter in children. Anticancer treatments appeared to have no impact on the trajectory of T1 SI. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:288-295.

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H-MR spectroscopy-A multi-center study.

PURPOSE: 3T magnetic resonance scanners have boosted clinical application of 1 H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi-center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors. METHODS: A total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques. RESULTS: Support vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi-center dataset from 1.5T magnets with echo time 20 to 32 ms alone. CONCLUSION: This study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359-2366, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Prospective multicentre evaluation and refinement of an analysis tool for magnetic resonance spectroscopy of childhood cerebellar tumours.

BACKGROUND: A tool for diagnosing childhood cerebellar tumours using magnetic resonance (MR) spectroscopy peak height measurement has been developed based on retrospective analysis of single-centre data. OBJECTIVE: To determine the diagnostic accuracy of the peak height measurement tool in a multicentre prospective study, and optimise it by adding new prospective data to the original dataset. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) and single-voxel MR spectroscopy were performed on children with cerebellar tumours at three centres. Spectra were processed using standard scanner software and peak heights for N-acetyl aspartate, creatine, total choline and myo-inositol were measured. The original diagnostic tool was used to classify 26 new tumours as pilocytic astrocytoma, medulloblastoma or ependymoma. These spectra were subsequently combined with the original dataset to develop an optimised scheme from 53 tumours in total. RESULTS: Of the pilocytic astrocytomas, medulloblastomas and ependymomas, 65.4% were correctly assigned using the original tool. An optimized scheme was produced from the combined dataset correctly assigning 90.6%. Rare tumour types showed distinctive MR spectroscopy features. CONCLUSION: The original diagnostic tool gave modest accuracy when tested prospectively on multicentre data. Increasing the dataset provided a diagnostic tool based on MR spectroscopy peak height measurement with high levels of accuracy for multicentre data.

Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome.

BACKGROUND: Despite previous identification of pre-operative clinical and radiological predictors of post-operative paediatric cerebellar mutism syndrome (CMS), a unifying pre-operative risk stratification model for use during surgical consent is currently lacking. The aim of the project is to develop a simple imaging-based pre-operative risk scoring scheme to stratify patients in terms of post-operative CMS risk. METHODS: Pre-operative radiological features were recorded for a retrospectively assembled cohort of 89 posterior fossa tumour patients from two major UK treatment centers (age 2-23yrs; gender 28 M, 61 F; diagnosis: 38 pilocytic astrocytoma, 32 medulloblastoma, 12 ependymoma, 1 high grade glioma, 1 pilomyxoid astrocytoma, 1 atypical teratoid rhabdoid tumour, 1 hemangioma, 1 neurilemmoma, 2 oligodendroglioma). Twenty-six (29%) developed post-operative CMS. Based upon results from univariate analysis and C4.5 decision tree, stepwise logistic regression was used to develop the optimal model and generate risk scores. RESULTS: Univariate analysis identified five significant risk factors and C4.5 decision tree analysis identified six predictors. Variables included in the final model are MRI primary location, bilateral middle cerebellar peduncle involvement (invasion and/or compression), dentate nucleus invasion and age at imaging >12.4 years. This model has an accuracy of 88.8% (79/89). Using risk score cut-off of 203 and 238, respectively, allowed discrimination into low (38/89, predicted CMS probability <3%), intermediate (17/89, predicted CMS probability 3-52%) and high-risk (34/89, predicted CMS probability ≥52%). CONCLUSIONS: A risk stratification model for post-operative paediatric CMS could flag patients at increased or reduced risk pre-operatively which may influence strategies for surgical treatment of cerebellar tumours. Following future testing and prospective validation, this risk scoring scheme will be proposed for use during the surgical consenting process.

Imaging central veins in brain lesions with 3-T T2*-weighted magnetic resonance imaging differentiates multiple sclerosis from microangiopathic brain lesions.

BACKGROUND: White matter lesions are frequently detected using brain magnetic resonance imaging (MRI) performed for various indications. Most are microangiopathic, but demyelination, including multiple sclerosis (MS), is an important cause; conventional MRI cannot always distinguish between these pathologies. The proportion of lesions with a central vein on 7-T T2*-weighted MRI prospectively distinguishes demyelination from microangiopathic lesions. OBJECTIVE: To test whether 3-T T2*-weighted MRI can differentiate MS from microangiopathic brain lesions. METHODS: A total of 40 patients were studied. Initially, a test cohort of 10 patients with MS and 10 patients with microangiopathic white matter lesions underwent 3-T T2*-weighted brain MRI. Anonymised scans were analysed blind to clinical data, and simple diagnostic rules were devised. These rules were applied to a validation cohort of 20 patients (13 with MS and 7 with microangiopathic lesions) by a blinded observer. RESULTS: Within the test cohort, all patients with MS had central veins visible in >45% of brain lesions, while the rest had central veins visible in <45% of lesions. By applying diagnostic rules to the validation cohort, all remaining patients were correctly categorised. CONCLUSION: 3-T T2*-weighted brain MRI distinguishes perivenous MS lesions from microangiopathic lesions. Clinical application of this technique could supplement existing diagnostic algorithms.

Assessing 'second-look' tumour resectability in childhood posterior fossa ependymoma-a centralised review panel and staging tool for future studies.

PURPOSE: To improve uniformity in radiological review/reporting and discussion of surgical resection status in the forthcoming SIOP Ependymoma II trial, a central review of imaging and resection status will be carried out prior to treatment stratification. We present a review of surgical decision-making from the UKCCSG/SIOP Ependymoma trial for very young children (<3 years) (Lancet Oncol 8:696-705, 2007) and propose a primary surgical staging system for residual disease that could be used for Ependymoma II. METHODS: Imaging of 89 patients enrolled in the UKCCSG infant Ependymoma trial was independently reviewed by 3 experienced paediatric neuro-oncology surgeons. Pre- and post-op MRI imaging was available for 28 posterior fossa cases with incomplete resection. Location of residual disease, decision to offer 'second-look' surgery (prior to adjuvant therapy), perceived chance of complete resection, and risk to cranial nerves was assessed. Recommendation for second-look surgery was compared with actual second-look surgery. RESULTS: In the actual study period, 13 patients (46 %) had further surgery at some point. The independent panel, after blinded review of the imaging, would have offered 19 patients overall (68 %) in this same cohort up-front early second-look surgery prior to definitive adjuvant therapy. We devised a 5-point staging system to introduce consistency in staging residual disease and resectability, and this is presented. CONCLUSIONS: Based on scans alone, a surgical panel would have offered second-look surgery to 68 % of patients with residual ependymoma. The potential benefits and drawbacks of a surgical review panel and classification system will be discussed in the context of the forthcoming Ependymoma II trial.

Fetal MRI demonstrating vein of Galen malformations in two successive pregnancies--a previously unreported occurrence.

PURPOSE: Vein of Galen malformations are rare and are usually detected in utero using ultrasonography. No definite genetic predisposition has been described in the literature. We present a case with two successive pregnancies complicated by vein of Galen malformations, which were assessed using fetal MRI. The putative role of genetic mutations is also discussed. METHODS: A 30-year-old primigravida presented in the third trimester with a fetus diagnosed with vein of Galen malformation on sonography. MRI and MR angiography were performed for further assessment. The subsequent pregnancy was again complicated by vein of Galen malformation. In addition to MRI, genetic analysis was carried out on both fetuses and on the parents. RESULTS: MR angiography revealed that both fetuses suffered from the choroidal sub-type of vein of Galen malformation, with multiple arterial feeders fistulating onto a midline venous pouch. The visualised anatomy obtained was far superior than on sonography and allowed categorisation of vein of Galen malformation sub-type. Genetic analysis on the mother and both fetuses showed variant RASA1 gene mutation. CONCLUSIONS: This case demonstrates that fetal MRI is a powerful tool in the investigation of in utero neurovascular malformations. A genetic mutation was identified, but this was of uncertain significance.

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.

Focal multiple sclerosis lesions abound in 'normal appearing white matter'.

BACKGROUND: The 'normal appearing white matter' (NAWM) in multiple sclerosis (MS) is known to be abnormal using quantitative magnetic resonance (MR) techniques. The aetiology of the changes in NAWM remains debatable. OBJECTIVE: To investigate whether high-field and ultra high-field T(1)-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) MRI enables detection of MS white matter lesions in areas defined as NAWM using high-field T(2)-weighted fluid attenuation inversion recovery (FLAIR) MRI; that is, to ascertain whether undetected lesions are likely contributors to the burden of abnormality in similarly defined NAWM. METHODS: Fourteen MS patients underwent MRI scans using 3T FLAIR and MPRAGE and 7 Tesla (7T) MPRAGE sequences. Independent observers identified lesions on 3T FLAIR and (7T and 3T) MPRAGE images. The detection of every individual lesion was then compared for each image type. RESULTS: We identified a total of 812 white matter lesions on 3T FLAIR. Using 3T MPRAGE, 186 additional lesions were detected that were not detected using 3T FLAIR. Using 7T MPRAGE, 231 additional lesions were detected that were not detected using 3T FLAIR. CONCLUSIONS: MRI with 3T and 7T MPRAGE enables detection of MS lesions in areas defined as NAWM using 3T FLAIR. Focal MS lesions contribute to the abnormalities known to exist in the NAWM.

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.

Droplet digital PCR-based detection of circulating tumor DNA from pediatric high grade and diffuse midline glioma patients.

BACKGROUND: The use of liquid biopsy is of potential high importance for children with high grade (HGG) and diffuse midline gliomas (DMG), particularly where surgical procedures are limited, and invasive biopsy sampling not without risk. To date, however, the evidence that detection of cell-free DNA (cfDNA) or circulating tumor DNA (ctDNA) could provide useful information for these patients has been limited, or contradictory. METHODS: We optimized droplet digital PCR (ddPCR) assays for the detection of common somatic mutations observed in pediatric HGG/DMG, and applied them to liquid biopsies from plasma, serum, cerebrospinal fluid (CSF), and cystic fluid collected from 32 patients. RESULTS: Although detectable in all biomaterial types, ctDNA presented at significantly higher levels in CSF compared to plasma and/or serum. When applied to a cohort of 127 plasma specimens from 41 patients collected from 2011 to 2018 as part of a randomized clinical trial in pediatric non-brainstem HGG/DMG, ctDNA profiling by ddPCR was of limited use due to the small volumes (mean = 0.49 mL) available. In anecdotal cases where sufficient material was available, cfDNA concentration correlated with disease progression in two examples each of poor response in H3F3A_K27M-mutant DMG, and longer survival times in hemispheric BRAF_V600E-mutant cases. CONCLUSION: Tumor-specific DNA alterations are more readily detected in CSF than plasma. Although we demonstrate the potential of the approach to assessing tumor burden, our results highlight the necessity for adequate sample collection and approach to improve detection if plasma samples are to be used.

WHO grade has no prognostic value in the pediatric high-grade glioma included in the HERBY trial.

BACKGROUND: The World Health Organization (WHO) adult glioma grading system is questionable in pediatric high-grade gliomas (pHGGs), which are biologically distinct from adult HGGs. We took advantage of the neuropathological review data obtained during one of the largest prospective randomized pHGG trials, namely HERBY (NCT01390948), to address this issue in children with newly diagnosed non-brainstem HGG. METHODS: HGG diagnosis was confirmed by pre-randomization, real-time central pathology review using WHO 2007 criteria, followed by a consensus review blinded to clinical factors and outcomes. We evaluated association between WHO 2007 grade and other clinical/radiological/biological characteristics and the prognostic value of WHO 2007 grade, midline location, and selected biomarkers (Ki-67 index/Olig2/CD34/EGFR/p53/H3F3A K27M mutation) on overall survival. RESULTS: Real-time central neuropathological review was feasible in a multicenter study, with a mean time of 2.4 days, and led to the rejection of HGG diagnosis in 20 of 163 cases (12.3%). The different grading criteria and resulting WHO grade were not significantly associated with overall survival in the entire population (n = 118) or in midline and non-midline subgroups. H3F3A K27M mutation was significantly associated with poor outcome. No significant prognostic value was observed for grade, even after regrading H3F3A K27M-mutated midline glioma as grade IV (WHO 2016). Midline location and a high Ki-67 index (≥20%) were associated with poor outcome (P = 0.004 and P = 0.04, respectively). A 10% increase in Ki-67 index was associated with a hazard ratio of 1.53 (95% CI: 1.27-1.83; P < 0.0001). CONCLUSION: Our findings suggest that WHO grade III versus IV has no prognostic value in pediatric HGG.

Radiological Evaluation of Newly Diagnosed Non-Brainstem Pediatric High-Grade Glioma in the HERBY Phase II Trial.

PURPOSE: The HERBY trial evaluated the benefit of the addition of the antiangiogenic agent Bevacizumab (BEV) to radiotherapy/temozolomide (RT/TMZ) in pediatric patients with newly diagnosed non-brainstem high-grade glioma (HGG). The work presented here aims to correlate imaging characteristics and outcome measures with pathologic and molecular data. EXPERIMENTAL DESIGN: Radiological, pathologic, and molecular data were correlated with trial clinical information to retrospectively re-evaluate event-free survival (EFS) and overall survival (OS). RESULTS: One-hundred thirteen patients were randomized to the RT/TMZ arm (n = 54) or the RT/TMZ+BEV (BEV arm; n = 59). The tumor arose in the cerebral hemispheres in 68 patients (Cerebral group) and a midline location in 45 cases (Midline group). Pathologic diagnosis was available in all cases and molecular data in 86 of 113. H3 K27M histone mutations were present in 23 of 32 Midline cases and H3 G34R/V mutations in 7 of 54 Cerebral cases. Total/near-total resection occurred in 44 of 68 (65%) Cerebral cases but in only 5 of 45 (11%) Midline cases (P < 0.05). Leptomeningeal metastases (27 cases, 13 with subependymal spread) at relapse were more frequent in Midline (17/45) than in Cerebral tumors (10/68, P < 0.05). Mean OS (14.1 months) and EFS (9.0 months) in Midline tumors were significantly lower than mean OS (20.7 months) and EFS (14.9 months) in Cerebral tumors (P < 0.05). Pseudoprogression occurred in 8 of 111 (6.2%) cases. CONCLUSIONS: This study has shown that the poor outcome of midline tumors (compared with cerebral) may be related to (1) lesser surgical resection, (2) H3 K27M histone mutations, and (3) higher leptomeningeal dissemination.