Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI

Up to 25% of children who undergo brain tumor resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterized by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in structures within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Longitudinal MRI datasets of 28 patients were acquired consisting of pre-, intra-, and postoperative scans. A semiautomated segmentation process was used to segment the ION on each MR image. A full set of imaging features describing the first- and second-order statistics and size of the ION were extracted for each image. Feature selection techniques were used to identify the most relevant features among the MRI features, demographics, and data based on neuroradiological assessment. A support vector machine was used to analyze the discriminative features selected by a generative k-nearest neighbor algorithm. The results indicate the presence of hyperintensity in the left ION as the most diagnostically relevant feature, providing a statistically significant improvement in the classification of patients (p=0.01) when using this feature alone

Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration

Background: The dentato-thalamo-cortical (DTC) pathway is recognized as the anatomical substrate for postoperative pediatric cerebellar mutism (POPCMS), a well-recognized complication affecting up to 31% of children undergoing posterior fossa brain tumour resection. The proximal structures of the DTC pathway also form a segment of the Guillain and Mollaret triangle, a neural network which when disrupted causes hypertrophic olivary degeneration (HOD) of the inferior olivary nucleus (ION). We hypothesize that there is an association between the occurrence of POPCMS and HOD and aim to evaluate this on MR imaging using qualitative and quantitative analysis of the ION in children with and without POPCMS. Methods: In this retrospective study we qualitatively analysed the follow up MR imaging in 48 children who underwent posterior fossa tumour resection for presence of HOD. Quantitative analysis of the ION was possible in 28 children and was performed using semi-automated segmentation followed by feature extraction and feature selection techniques and relevance of the features to POPCMS were evaluated. The diagnosis of POPCMS was made independently based on clinical and nursing assessment notes. Results: There was significant association between POPCMS and bilateral HOD (P=0.002) but not unilateral HOD. Quantitative analysis showed that hyperintensity in the left ION was the most relevant feature in children with POPCMS. Conclusions: Bilateral HOD can serve as a reliable radiological indicator in establishing the diagnosis of POPCMS particularly in equivocal cases. The strong association of signal change due to HOD in the left ION suggests that injury to the right proximal efferent cerebellar pathway plays an important role in the causation of POPCMS. Keywords: Cerebellar mutism syndrome (CMS); hypertrophic olivary degeneration; posterior fossa syndrome (PFS); postoperative pediatric cerebellar mutism syndrom

Progressive myelin oligodendrocyte glycoprotein-associated demyelination mimicking leukodystrophy

BackgroundMyelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) may be associated with relapsing disease, but clinical progression independent of relapse activity is rare.ObjectivesTo report progressive disease in a patient with MOGAD.MethodsA single retrospective case report.ResultsAt 4 years of age, the patient had a single episode of acute disseminated encephalomyelitis. She remained well until age 17 years but over the next 9 years developed progressive spastic quadriparesis, cognitive and bulbar dysfunction. Brain imaging showed a leukodystrophy-like pattern of white matter abnormality with contrast enhancement at different time points. Myelin oligodendrocyte glycoprotein (MOG)-IgG was repeatedly positive by live cell-based assay.ConclusionSecondary progression may be a rare presentation of MOG-IgG-associated disease

The utility of routine surveillance screening with magnetic resonance imaging (MRI) to detect tumour recurrence in children with low-grade central nervous system (CNS) tumours : a systematic review

Background: Magnetic resonance imaging (MRI) is routinely used as a surveillance tool to detect early asymptomatic tumour recurrence with a view to improving patient outcomes. This systematic review aimed to assess its utility in children with low-grade CNS tumours. Methods: Using standard systematic review methods, twelve databases were searched up to January 2017. Results: Seven retrospective case series studies (n = 370 patients) were included, with average follow-up ranging from 5.6 to 7 years. No randomised controlled trials (RCTs) were identified. Due to study heterogeneity only a descriptive synthesis could be undertaken. Imaging was most frequent in the first year post-surgery (with 2–4 scans) reducing to around half this frequency in year two and annually thereafter for the duration of follow-up. Diagnostic yield ranged from 0.25 to 2%. Recurrence rates ranged from 5 to 41%, with most recurrences asymptomatic (range 65–100%). Collectively, 56% of recurrences had occurred within the first year post-treatment (46% in the first 6-months), 68% by year two and 90% by year five. Following recurrence, 90% of patients underwent treatment changes, mainly repeat surgery (72%). Five-year OS ranged from 96 to 100%, while five-year recurrence-free survival ranged from 67 to 100%. None of the studies reported quality of life measures. Conclusion: This systematic review highlights the paucity of evidence currently available to assess the utility of MRI surveillance despite it being routine clinical practice and costly to patients, their families and healthcare systems. This needs to be evaluated within the context of an RCT

Missense variants in the N-terminal domain of the A isoform of FHF2/FGF13 cause an X-linked developmental and epileptic encephalopathy

Fibroblast growth factor homologous factors (FHFs) are intracellular proteins which regulate voltage-gated sodium (Na v) channels in the brain and other tissues. FHF dysfunction has been linked to neurological disorders including epilepsy. Here, we describe two sibling pairs and three unrelated males who presented in infancy with intractable focal seizures and severe developmental delay. Whole-exome sequencing identified hemi- and heterozygous variants in the N-terminal domain of the A isoform of FHF2 (FHF2A). The X-linked FHF2 gene (also known as FGF13) has alternative first exons which produce multiple protein isoforms that differ in their N-terminal sequence. The variants were located at highly conserved residues in the FHF2A inactivation particle that competes with the intrinsic fast inactivation mechanism of Na v channels. Functional characterization of mutant FHF2A co-expressed with wild-type Na v1.6 (SCN8A) revealed that mutant FHF2A proteins lost the ability to induce rapid-onset, long-term blockade of the channel while retaining pro-excitatory properties. These gain-of-function effects are likely to increase neuronal excitability consistent with the epileptic potential of FHF2 variants. Our findings demonstrate that FHF2 variants are a cause of infantile-onset developmental and epileptic encephalopathy and underline the critical role of the FHF2A isoform in regulating Na v channel function

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 (rCBV uncorr) and corrected (rCBV corr) relative cerebral blood volume, and the leakage parameter, K 2. 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 rCBV uncorr 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 K 2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBV uncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K 2 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

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

BackgroundThe 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).MethodsMetabolite 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.FindingsGroup-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).InterpretationTissue 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

IMG-06. PREDICTING SURVIVAL FROM PERFUSION AND DIFFUSION MRI BY MACHINE LEARNING

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