Switching from linear to macrocyclic gadolinium‐based contrast agents halts the relative T 1 ‐Weighted signal increase in deep gray matter of children with brain tumors: A retrospective study

BackgroundStudies have shown signal intensity (SI) changes in the brains of children exposed to repeated doses of a gadolinium‐based contrast agent (GBCA).HypothesisThe 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 TypeRetrospective longitudinal.PopulationThirty‐five children, age range 0.5–17.0 years, undergoing brain tumor follow‐up between 2006 and 2017.Field Strength/SequenceUnenhanced T1WI, serial scans at both 1.5T and 3T.AssessmentRegions 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 TestsOne‐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.ResultsA 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 ConclusionSwitching 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

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

AbstractBackgroundA 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.ObjectiveTo 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 methodsMagnetic 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.ResultsOf 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.ConclusionThe 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

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

Magnetic resonance imaging protocols for paediatric neuroradiology

Increasingly, radiologists are encouraged to have protocols for all imaging studies and to include imaging guidelines in care pathways set up by the referring clinicians. This is particularly advantageous in MRI where magnet time is limited and a radiologist’s review of each patient’s images often results in additional sequences and longer scanning times without the advantage of improvement in diagnostic ability. The difficulties of imaging small children and the challenges presented to the radiologist as the brain develops are discussed. We present our protocols for imaging the brain and spine of children based on 20 years experience of paediatric neurological MRI. The protocols are adapted to suit children under the age of 2 years, small body parts and paediatric clinical scenarios

Molecular, pathological, radiological, and immune profiling of non-brainstem pediatric high-grade glioma from the HERBY phase II randomized trial

The HERBY trial was a phase II open-label, randomized, multicenter trial evaluating bevacizumab (BEV) in addition to temozolomide/radiotherapy in patients with newly diagnosed non-brainstem high-grade glioma (HGG) between the ages of 3 and 18 years. We carried out comprehensive molecular analysis integrated with pathology, radiology, and immune profiling. In post-hoc subgroup analysis, hypermutator tumors (mismatch repair deficiency and somatic POLE/POLD1 mutations) and those biologically resembling pleomorphic xanthoastrocytoma ([PXA]-like, driven by BRAF_V600E or NF1 mutation) had significantly more CD8+ tumor-infiltrating lymphocytes, and longer survival with the addition of BEV. Histone H3 subgroups (hemispheric G34R/V and midline K27M) had a worse outcome and were immune cold. Future clinical trials will need to take into account the diversity represented by the term ‘‘HGG’’ in the pediatric population

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 mm 2 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

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 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

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 probabilit