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Background: Cerebellar mutism syndrome (CMS) is a common and debilitating complication of posterior fossa tumor surgery in children. Affected children exhibit communication and social impairments that overlap phenomenologically with subsets of deficits exhibited by children with Autism spectrum disorder (ASD). Although both CMS and ASD are thought to involve disrupted cerebro-cerebellar circuitry, they are considered independent conditions due to an incomplete understanding of their shared neural substrates., Methods: In this study, we analyzed postoperative cerebellar lesions from 90 children undergoing posterior fossa resection of medulloblastoma, 30 of whom developed CMS. Lesion locations were mapped to a standard atlas, and the networks functionally connected to each lesion were computed in normative adult and pediatric datasets. Generalizability to ASD was assessed using an independent cohort of children with ASD and matched controls (n = 427)., Results: Lesions in children who developed CMS involved the vermis and inferomedial cerebellar lobules. They engaged large-scale cerebellothalamocortical circuits with a preponderance for the prefrontal and parietal cortices in the pediatric and adult connectomes, respectively. Moreover, with increasing connectomic age, CMS-associated lesions demonstrated stronger connectivity to the midbrain/red nuclei, thalami and inferior parietal lobules and weaker connectivity to the prefrontal cortex. Importantly, the CMS-associated lesion network was independently reproduced in ASD and correlated with communication and social deficits, but not repetitive behaviors., Conclusions: Our findings indicate that CMS-associated lesions may result in an ASD-like network disturbance that occurs during sensitive windows of brain development. A common network disturbance between CMS and ASD may inform improved treatment strategies for affected children., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Published in 2021, the fifth edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) introduced new molecular criteria for tumor types that commonly occur in either pediatric or adult age groups. Adolescents and young adults (AYAs) are at the intersection of adult and pediatric care, and both pediatric-type and adult-type CNS tumors occur at that age. Mortality rates for AYAs with CNS tumors have increased by 0.6% per year for males and 1% per year for females from 2007 to 2016. To best serve patients, it is crucial that both pediatric and adult radiologists who interpret neuroimages are familiar with the various pediatric- and adult-type brain tumors and their typical imaging morphologic characteristics. Gliomas account for approximately 80% of all malignant CNS tumors in the AYA age group, with the most common types observed being diffuse astrocytic and glioneuronal tumors. Ependymomas and medulloblastomas also occur in the AYA population but are seen less frequently. Importantly, biologic behavior and progression of distinct molecular subgroups of brain tumors differ across ages. This review discusses newly added or revised gliomas in the fifth edition of the CNS WHO classification, as well as other CNS tumor types common in the AYA population., (© RSNA, 2024.)

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless ( WNT ) and non-sonic hedgehog ( SHH ) MB and then differentiates therapeutically relevant WNT from SHH . Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT . A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Background: Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis., Objective: To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP., Methods: We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score., Results: Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179., Conclusion: An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning., (© Congress of Neurological Surgeons 2021.)

Background: Cerebellar mutism syndrome (CMS) is a common complication following resection of posterior fossa tumors, most commonly after surgery for medulloblastoma. Medulloblastoma subgroups have historically been treated as a single entity when assessing CMS risk; however, recent studies highlighting their clinical heterogeneity suggest the need for subgroup-specific analysis. Here, we examine a large international multicenter cohort of molecularly characterized medulloblastoma patients to assess predictors of CMS., Methods: We assembled a cohort of 370 molecularly characterized medulloblastoma subjects with available neuroimaging from 5 sites globally, including Great Ormond Street Hospital, Christian Medical College and Hospital, the Hospital for Sick Children, King Hussein Cancer Center, and Lucile Packard Children's Hospital. Age at diagnosis, sex, tumor volume, and CMS development were assessed in addition to molecular subgroup., Results: Overall, 23.8% of patients developed CMS. CMS patients were younger (mean difference -2.05 years ± 0.50, P = 0.0218) and had larger tumors (mean difference 10.25 cm3 ± 4.60, P = 0.0010) that were more often midline (odds ratio [OR] = 5.72, P < 0.0001). In a multivariable analysis adjusting for age, sex, midline location, and tumor volume, Wingless (adjusted OR = 4.91, P = 0.0063), Group 3 (adjusted OR = 5.56, P = 0.0022), and Group 4 (adjusted OR = 8.57 P = 9.1 × 10-5) tumors were found to be independently associated with higher risk of CMS compared with sonic hedgehog tumors., Conclusions: Medulloblastoma subgroup is a very strong predictor of CMS development, independent of tumor volume and midline location. These findings have significant implications for management of both the tumor and CMS., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Purpose: Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma., Materials and Methods: In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance., Results: Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma., Conclusions: This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups., (© 2019 by American Journal of Neuroradiology.)

Background and Purpose: Molecular grouping of medulloblastoma correlates with prognosis and supports the therapeutic strategy. We provide our experience with the imaging features of primary and metastatic disease in relation to the molecular groups., Materials and Methods: One hundred nineteen consecutive patients (mean age, 7.3 ± 3.8 years at diagnosis; male, 79 [66.4%]) with a confirmed diagnosis of medulloblastoma and interpretable pretreatment MRIs were retrieved from our data base from January 2000 to December 2016. Each patient was assigned to wingless, sonic hedgehog, group 3, or group 4 molecular groups. Then, we determined the imaging features of both primary and metastatic/recurrent disease predictive of molecular groups., Results: In addition to recently reported predictors based on primary tumor, including cerebellar peripheral location for sonic hedgehog (adjusted odds ratio = 9, P < .0001), minimal enhancement of primary group 4 tumor (adjusted odds ratio = 5.2, P < .0001), and cerebellopontine angle location for wingless (adjusted odds ratio = 1.4, P = .03), ependymal metastasis with diffusion restriction and minimal postcontrast enhancement ("mismatching pattern") (adjusted odds ratio = 2.8, P = .001) for group 4 and spinal metastasis for group 3 (adjusted odds ratio = 1.9, P = .01) also emerged as independent predictors of medulloblastoma molecular groups. Specifically, the presence of a metastasis in the third ventricular infundibular recess showing a mismatching pattern was significantly associated with group 4 ( P = .02)., Conclusions: In addition to imaging features of primary tumors, some imaging patterns of metastatic dissemination in medulloblastoma seem characteristic, perhaps even specific to certain groups. This finding could further help in differentiating molecular groups, specifically groups 3 and 4, when the characteristics of the primary tumor overlap., (© 2018 by American Journal of Neuroradiology.)

OBJECTIVE Metastatic dissemination is a major treatment challenge and cause of death in patients with medulloblastoma. However, the influence of molecular biology on the pattern of metastatic dissemination at diagnosis is not known. In this study, the authors sought to define the location, pattern, and imaging characteristics of medulloblastoma metastases across subgroups at diagnosis. METHODS A consecutive cohort of patients with metastatic medulloblastoma at The Hospital for Sick Children and the University Hospital Motol, who underwent up-front MRI of the craniospinal axis, was assembled and allocated to subgroups using NanoString limited gene-expression profiling. Radiological characteristics (including location, morphology, size, diffusion restriction, and contrast enhancement) were discerned through a retrospective review. RESULTS Forty metastatic medulloblastomas were identified with up-front neuroimaging of the craniospinal axis: 5 sonic hedgehog (SHH), 16 Group 3, and 19 Group 4 metastases. Significant subgroup-specific differences were observed, particularly with respect to tumor location, size, and morphology. Group 3 metastases were most frequently laminar compared with a more nodular pattern in Group 4 (14 of 16 in Group 3 vs 8 of 19 in Group 4; p = 0.0004). Laminar metastases were not observed in patients with SHH medulloblastoma. Suprasellar metastases are highly specific to Group 4 (p = 0.016). Two of the 5 SHH cases had multifocal lesions in the cerebellum, raising the possibility that these were in fact synchronous primary tumors and not true metastases. A minority of patients with Group 4 metastases harbored metastatic deposits that did not enhance on MRI after contrast administration, often in patients whose primary tumor did not enhance. CONCLUSIONS The location, morphology, and imaging characteristics of metastatic medulloblastoma differ across molecular subgroups, with implications for diagnosis and management. This suggests that the biology of leptomeningeal dissemination differs among medulloblastoma subgroups.

Executive functions (EFs) are involved in the attainment, maintenance, and integration of information; these functions may play a key role in cognitive and behavioural outcomes in children treated for medulloblastoma (MB). At present, it remains unclear which EFs are most sensitive to the treatment effects for MB and whether damage to cerebrocerebellar circuitry is associated with EF. We completed a comprehensive evaluation of EF in 24 children treated for MB and 20 age-matched healthy children (HC) and distilled these measures into components. Six components (C1-C6) were extracted from our model, reflecting dissociable constructs of EF: C1 = cognitive efficiency; C2 = planning/problem-solving; C3 = positive cognitive emotion regulation; C4 = working memory; C5 = negative cognitive emotion regulation; and C6 = mixed cognitive emotion regulation. Group differences were found for C1, C2, C3, and C4; the MB group showed poorer performance on EF tasks and made less use of positive cognitive emotion regulation strategies relative to HC. Compromise to cerebrocerebellar microstructure - cerebro-ponto-cerebellar and cerebello-thalamo-cerebral pathways - was evident in children treated for MB compared to HC. We found that cerebrocerebellar circuitry has a mediating effect on one component of EF following treatment for MB - working memory., (© 2015 The British Psychological Society.)

OBJECTIVE Craniospinal irradiation damages the white matter in children treated for medulloblastoma, but the treatment-intensity effects are unclear. In a cross-sectional retrospective study, the effects of treatment with the least intensive radiation protocol versus protocols that delivered more radiation to the brain, in addition to the effects of continuous radiation dose, on white matter architecture were evaluated. METHODS Diffusion tensor imaging was used to assess fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. First, regional white matter analyses and tract-based spatial statistics were conducted in 34 medulloblastoma patients and 38 healthy controls. Patients were stratified according to those treated with 1) the least intensive radiation protocol, specifically reduced-dose craniospinal irradiation plus a boost to the tumor bed only (n = 17), or 2) any other dose and boost combination that delivered more radiation to the brain, which was also termed the "all-other-treatments" group (n = 17), and comprised patients treated with standard-dose craniospinal irradiation plus a posterior fossa boost, standard-dose craniospinal irradiation plus a tumor bed boost, or reduced-dose craniospinal irradiation plus a posterior fossa boost. Second, voxel-wise dose-distribution analyses were conducted on a separate cohort of medulloblastoma patients (n = 15). RESULTS The all-other-treatments group, but not the reduced-dose craniospinal irradiation plus tumor bed group, had lower fractional anisotropy and higher radial diffusivity than controls in all brain regions (all p < 0.05). The reduced-dose craniospinal irradiation plus tumor bed boost group had higher fractional anisotropy (p = 0.05) and lower radial diffusivity (p = 0.04) in the temporal region, and higher fractional anisotropy in the frontal region (p = 0.04), than the all-other-treatments group. Linear mixed-effects modeling revealed that the dose and age at diagnosis together 1) better predicted fractional anisotropy in the temporal region than models with either alone (p < 0.005), but 2) did not better predict fractional anisotropy in comparison with dose alone in the occipital region (p > 0.05). CONCLUSIONS Together, the results show that white matter damage has a clear association with increasing radiation dose, and that treatment with reduced-dose craniospinal irradiation plus tumor bed boost appears to preserve white matter in some brain regions.

Detailed information regarding the neuroanatomy of reciprocal cerebrocerebellar pathways is based on well-documented animal models. This knowledge has not yet been fully translated to humans, in that the structure of reciprocal cerebrocerebellar pathways connecting the cerebellum with frontal lobe has not been shown in its entirety. We investigated the impact of injury and age on cerebrocerebellar pathway microstructure using diffusion tensor imaging (DTI) and probabilistic tractography. We used medulloblastoma (MB) as an injury model due to the known impact of tumor/treatment on the cerebellum, one of the main nodes of cerebrocerebellar pathways. We delineated and segmented reciprocal cerebrocerebellar pathways connecting the cerebellum with frontal lobe in 38 healthy children (HC) and 34 children treated for MB, and compared pathway segment DTI measures between HC and MB and across three age cohorts: childhood, early adolescence, and late adolescence. Pathway compromise was evident for the MB group compared to HC, particularly within posterior segments (Ps<0.01). Though we found no age effect, group differences in microstructure were driven by pathway segment (posterior) and age cohort (adolescence), which may reflect the extent of injury to the posterior fossa following treatment for MB and age cohort differences in radiation treatment protocol in our sample. We have examined the microstructure of reciprocal cerebrocerebellar connections in the pediatric brain and have found that these pathways are injured in MB, a clinical population treated with surgery, radiation, and chemotherapy. Our findings support the late effects literature describing white matter injury emergence in the years following treatment for MB., (© 2015 Wiley Periodicals, Inc.)

Children treated for medulloblastoma (MB) exhibit long-term impairments in declarative memory, but the pathophysiology underlying this is unclear. Previous studies report declines in global white matter volume, but have failed to link this to declines in memory performance. We examined the effects of treatment on measures of global brain structure (i.e., total white and gray matter volume) and specific memory structures (i.e., hippocampus and uncinate fasciculus). We used volumetric MRI and diffusion tensor imaging in pediatric survivors of MB and one survivor of astrocytoma treated with cranial-spinal radiation (n = 20), and healthy controls (n = 13). Compared to controls, the survivor group exhibited reduced white matter volume, damage to the uncinate fasciculus, and a smaller right hippocampus. Critically, reduced hippocampal volume was not related to differences in brain volume, suggesting that the hippocampus may be especially vulnerable to treatment effects. A subset of the survivors (n = 10) also underwent memory testing using the Children's Memory Scale (CMS). Performance on the general index of the CMS was significantly correlated with measures of hippocampal volume and uncinate fasciculus. The examination of treatment effects on specific brain regions provides a better understanding of long-term cognitive outcome in children with brain tumors, particularly medulloblastoma.

A constellation of deficits, termed the cerebellar cognitive affective syndrome (CCAS), has been reported following acquired cerebellar lesions. We studied emotion identification and the cognitive control of emotion in children treated for acquired tumors of the cerebellum. Participants were 37 children (7-16 years) treated for cerebellar tumors (19 benign astrocytomas (AST), 18 malignant medulloblastomas (MB), and 37 matched controls (CON). The Emotion Identification Task investigated recognition of happy and sad emotions in music. In two cognitive control tasks, we investigated whether children could identify emotion in situations in which the emotion in the music and the emotion in the lyrics was either congruent or incongruent. Children with cerebellar tumors identified emotion as accurately and quickly as controls (p > .05), although there was a significant interaction of emotions and group (p < .01), with the MB group performing less accurately identifying sad emotions, and both cerebellar tumor groups were impaired in the cognitive control of emotions (p < .01). The fact that childhood acquired cerebellar tumors disrupt cognitive control of emotion rather than emotion identification provides some support for a model of the CCAS as a disorder, not so much of emotion as of the regulation of emotion by cognition.

Treatment of children with cranial-spinal radiation (CSR) for brain tumors is associated with adverse intellectual outcome and white matter damage. However, the correlation between IQ and measures of white matter integrity has received little attention. We examined apparent diffusion coefficient (ADC), fractional anisotropy (FA), and intelligence in pediatric patients treated with CSR for medulloblastoma relative to control subjects. ADC and FA measures were obtained for eight patients and eight control children and evaluated in multiple regions of interest in the cerebral hemispheres. Mean ADC and mean FA for each region were calculated, group differences were evaluated, and the relationship between these measures and intelligence were examined. In our study group, decreased IQ was associated with increased ADC and decreased FA (P < 0.01). Mean IQ for the CSR group was lower than that for the control group, but the difference was not significant when controlling for overall mean FA or ADC (P > 0.10). Overall mean FA was lower and ADC was higher in the CSR group relative to controls (P < 0.01). Specifically, FA was lower in the genu of the corpus callosum, the anterior and posterior limbs of the internal capsule, inferior frontal white matter, and high frontal white matter, and ADC was higher in all regions in patients relative to controls (P < 0.01). Compromised white matter integrity was observed for multiple regions within the cerebral hemispheres following CSR. A novel finding was that microscopic damage in normal-appearing white matter, as indexed by higher ADC and lower FA, was related to poor intellectual outcome relative to age-matched controls.