Distinctive MRI features of pediatric medulloblastoma subtypes

Pediatric Hemispheric High-Grade Gliomas and H3.3-G34 Mutation: A Review of the Literature on Biological Features and New Therapeutic Strategies

Pediatric high-grade glioma (pHGG) encompasses a wide range of gliomas with different genomic, epigenomic, and transcriptomic features. Almost 50% of pHGGs present a mutation in genes coding for histone 3, including the subtype harboring the H3.3-G34 mutation. In this context, histone mutations are frequently associated with mutations in TP53 and ATRX, along with PDGFRA and NOTCH2NL amplifications. Moreover, the H3.3-G34 histone mutation induces epigenetic changes in immune-related genes and exerts modulatory functions on the microenvironment. Also, the functionality of the blood-brain barrier (BBB) has an impact on treatment response. The prognosis remains poor with conventional treatments, thus eliciting the investigation of additional and alternative therapies. Promising molecular targets include PDGFRA amplification, BRAF mutation, EGFR amplification, NF1 loss, and IDH mutation. Considering that pHGGs harboring the H3.3-G34R mutation appear to be more susceptible to immunotherapies (ITs), different options have been recently explored, including immune checkpoint inhibitors, antibody mediated IT, and Car-T cells. This review aims to summarize the knowledge concerning cancer biology and cancer-immune cell interaction in this set of pediatric gliomas, with a focus on possible therapeutic options.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

RATIONALE AND OBJECTIVES

Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

MATERIALS AND METHODS

Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center.

RESULTS

31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models.

CONCLUSION

The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures

Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma

RATIONALE AND OBJECTIVES

Despite advances in risk-stratified treatment strategies for children with medulloblastoma (MB), the prognosis for MB with short-term recurrence is extremely poor, and there is still a lack of evaluation of short-term recurrence risk or short-term survival. This study aimed to construct and validate a radiomics model for predicting the outcome of MB based on preoperative multiparametric magnetic resonance images (MRIs) and to provide an objective for clinical decision-making.

MATERIALS AND METHODS

The clinical and imaging data of 64 patients with MB admitted to Shenzhen Children's Hospital from December 2012 to December 2021 and confirmed by pathology were retrospectively collected. According to the 18-month progression-free survival, the cases were classified into a good prognosis group and a poor prognosis group, and all cases were divided into training group (70%) and validation group (30%) randomly. Radiomics features were extracted from MRI of each child. The consistency test, t-test, and the least absolute shrinkage and selection operator were used for feature selection. The support vector machine (SVM) and receiver operator characteristic were used to evaluate the distinguishing ability of the selected features to the prognostic groups. RAD score was calculated based on the selected features. The clinical characteristics and RAD score were included in the multivariate logistic regression, and prediction models were constructed by screening out independent influences. The radiomics nomogram was constructed, and its clinical significance was evaluated.

RESULTS

A total of 1930 radiomic features were extracted from the images of each patient, and 11 features were included in the construction of radiomics score after selected. The area under the curve (AUC) values of the SVM model in the training and validation groups were 0.946 and 0.797, respectively. The radiomics nomogram was constructed based on the training cohort, and the AUC values in the training group and the validation group were 0.926 and 0.835, respectively. The results of clinical decision curve analysis showed that a good net benefit could be obtained from the nomogram.

CONCLUSION

The radiomics nomogram established based on MRI can be used as a noninvasive predictive tool to evaluate the prognosis of children with MB, which is expected to help neurosurgeons better conduct preoperative planning and patient follow-up management.

Brain Tumor Imaging in Adolescents and Young Adults: 2021 WHO Updates for Molecular-based Tumor Types

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.

Extra-axial desmoplastic/nodular medulloblastoma in adult mimicking cerebellar metastasis: reappraisal of this rare presentation with literature review

Introduction and importance

Medulloblastomas are the most common malignant intra-axial brain tumour in paediatric patients and represent 35-40% of posterior fossa tumour types in children between 3 and 9 years of age. Medulloblastomas may also be found in adulthood. These tumours are classified into two groups according to its molecular characteristics and histological type. The desmoplastic/nodular subtype is the second common subtype after the classic one. Only three cases of desmoplastic/nodular extra-axial medelloblastoma have been previously reported in the literature originating from to the cerebellopontine angle.

Case presentation

The authors report a new case of an extra-axial desmoplastic/nodular cerebellar medulloblastoma originating outside the cerebellopontine angle and mimicking a solitary cerebellar metastasis in a 49-year-old female patient who presented for a raised intracranial pressure and cerebellar syndrome.

Clinical discussion

Medulloblastoma is a malignant embryonal intra-axial tumour of the cerebellum or posterior brain stem that occurs mainly in children. Medulloblastomas may also be found in adulthood. Desmoplastic/nodular medulloblastoma is the second most common type of all medulloblastomas. The intra-axial form is always predominant. Only three cases of extra-axial desmoplastic/nodular medulloblastoma have been reported in the literature. The authors will go through the literature to dissect this rare entity.

Conclusion

Although considered a common paediatric intra-axial tumour, there are increasing numbers of solitary cases reporting an extra-axial presentation in different locations of the posterior cerebral fossa even in adulthood. These rare and unusual presentations and locations may mislead the correct diagnosis and delay treatment.

The Site of Origin of Medulloblastoma: Surgical Observations Correlated to Molecular Groups

Developmental gene expression data from medulloblastoma (MB) suggest that WNT-MB originates from the region of the embryonic lower rhombic lip (LRL), whereas SHH-MB and non-WNT/non-SHH MB arise from cerebellar precursor matrix regions. This study aimed to analyze detailed intraoperative data with regard to the site of origin (STO) and compare these findings with the hypothesized regions of origin associated with the molecular group. A review of the institutional database identified 58 out of 72 pediatric patients who were operated for an MB at our department between 1996 and 2020 that had a detailed operative report and a surgical video as well as clinical and genetic classification data available for analysis. The STO was assessed based on intraoperative findings. Using the intraoperatively defined STO, "correct" prediction of molecular groups was feasible in 20% of WNT-MB, 60% of SHH-MB and 71% of non-WNT/non-SHH MB. The positive predictive values of the neurosurgical inspection to detect the molecular group were 0.21 (95% CI 0.08-0.48) for WNT-MB, 0.86 (95% CI 0.49-0.97) for SHH-MB and 0.73 (95% CI 0.57-0.85) for non-WNT/non-SHH MB. The present study demonstrated a limited predictive value of the intraoperatively observed STO for the prediction of the molecular group of MB.

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction

Aim

Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment.

Methods

This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance.

Results

Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)].

Conclusions

The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology.

MRI findings of primary intracranial sarcomas in children

BACKGROUND

Primary intracranial sarcoma is a very rare high-grade tumor. Scant reports exist on this malignancy in children, which limit the information about its imaging characteristics.

OBJECTIVE

We aimed to describe the main characteristics of primary intracranial sarcoma on MRI.

MATERIALS AND METHODS

In this cross-sectional descriptive observational study, we reviewed 18 patients (aged 1-18 years) with primary intracranial sarcomas diagnosed between 2015 and 2021.

RESULTS

The main findings were contrast enhancement (100%), diffusion restriction (78%), hemorrhage (89%), meningeal extension (67%), necrosis (67%), and supratentorial location (72%).

CONCLUSION

Primary intracranial sarcoma are typically supratentorial in location. MRI findings of primary intracranial sarcoma are similar to other intracranial malignancies.

A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part 2-Newly Described and Revised Tumor Types

The fifth edition of the World Health Organization classification of tumors of the central nervous system (CNS), published in 2021, introduces major shifts in the classification of brain and spine tumors. These changes were necessitated by rapidly increasing knowledge of CNS tumor biology and therapies, much of which is based on molecular methods in tumor diagnosis. The growing complexity of CNS tumor genetics has required reorganization of tumor groups and acknowledgment of new tumor entities. For radiologists interpreting neuroimaging studies, proficiency with these updates is critical in providing excellent patient care. This review will focus on new or revised CNS tumor types and subtypes, beyond infiltrating glioma (described in part 1 of this series), with an emphasis on imaging features.

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance

BACKGROUND

Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors.

PURPOSE

To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies.

MATERIAL AND METHODS

A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed.

RESULTS

Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively.

CONCLUSION

Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

Changes to pediatric brain tumors in 2021 World Health Organization classification of tumors of the central nervous system

New tumor types are continuously being described with advances in molecular testing and genomic analysis resulting in better prognostics, new targeted therapy options and improved patient outcomes. As a result of these advances, pathological classification of tumors is periodically updated with new editions of the World Health Organization (WHO) Classification of Tumors books. In 2021, WHO Classification of Tumors of the Central Nervous System, 5^th edition (CNS5), was published with major changes in pediatric brain tumors officially recognized including pediatric gliomas being separated from adult gliomas, ependymomas being categorized based on anatomical compartment and many new tumor types, most of them seen in children. Additional general changes, such as tumor grading now being done within tumor types rather than across entities and changes in definition of glioblastoma, are also relevant to pediatric neuro-oncology practice. The purpose of this manuscript is to highlight the major changes in pediatric brain tumors in CNS5 most relevant to radiologists. Additionally, brief descriptions of newly recognized entities will be presented with a focus on imaging findings.

Addressing blood-brain-tumor-barrier heterogeneity in pediatric brain tumors with innovative preclinical models

Brain tumors represent the leading cause of disease-related mortality and morbidity in children, with effective treatments urgently required. One factor limiting the effectiveness of systemic therapy is the blood-brain-barrier (BBB), which limits the brain penetration of many anticancer drugs. BBB integrity is often compromised in tumors, referred to as the blood-brain-tumor-barrier (BBTB), and the impact of a compromised BBTB on the therapeutic sensitivity of brain tumors has been clearly shown for a few selected agents. However, the heterogeneity of barrier alteration observed within a single tumor and across distinct pediatric tumor types represents an additional challenge. Herein, we discuss what is known regarding the heterogeneity of tumor-associated vasculature in pediatric brain tumors. We discuss innovative and complementary preclinical model systems that will facilitate real-time functional analyses of BBTB for all pediatric brain tumor types. We believe a broader use of these preclinical models will enable us to develop a greater understanding of the processes underlying tumor-associated vasculature formation and ultimately more efficacious treatment options.

The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups

BACKGROUND

Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce.

OBJECTIVE

This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features.

MATERIALS AND METHODS

In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups.

RESULTS

We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found.

CONCLUSION

ADC histogram analysis can be implemented as a complementary tool in the preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively.

Common and uncommon neuroimaging manifestations of ataxia: an illustrated guide for the trainee radiologist. Part 2 - neoplastic, congenital, degenerative, and hereditary diseases

Abstract Ataxia is defined as a lack of coordination of voluntary movement, caused by a variety of factors. Ataxia can be classified by the age at onset and type (chronic or acute). The causative lesions involve the cerebellum and cerebellar connections. The correct, appropriate use of neuroimaging, particularly magnetic resonance imaging, can make the diagnosis relatively straightforward and facilitate implementation of the appropriate clinical management. The purpose of this pictorial essay is to describe the imaging findings of ataxia, based on cases obtained from the archives of a tertiary care hospital, with a review of the most important findings. We also discuss and review the imaging aspects of neoplastic diseases, malformations, degenerative diseases, and hereditary diseases related to ataxia.

New Developments in the Pathogenesis, Therapeutic Targeting, and Treatment of Pediatric Medulloblastoma

Pediatric medulloblastoma (MB) is the most common pediatric brain tumor with varying prognoses depending on the distinct molecular subtype. The four consensus subgroups are WNT, Sonic hedgehog (SHH), Group 3, and Group 4, which underpin the current 2021 WHO classification of MB. While the field of knowledge for treating this disease has significantly advanced over the past decade, a deeper understanding is still required to improve the clinical outcomes for pediatric patients, who are often vulnerable in ways that adult patients are not. Here, we discuss how recent insights into the pathogenesis of pediatric medulloblastoma have directed current and future research. This review highlights new developments in understanding the four molecular subtypes’ pathophysiology, epigenetics, and therapeutic targeting. In addition, we provide a focused discussion of recent developments in imaging, and in the surgery, chemotherapy, and radiotherapy of pediatric medulloblastoma. The article includes a brief explanation of healthcare costs associated with medulloblastoma treatment.

Multidisciplinary Management of Medulloblastoma: Consensus, Challenges, and Controversies

Medulloblastoma is a highly aggressive "small round blue cell tumor" of the posterior fossa predominantly seen in children. Historically aggressive multimodality regimens have achieved encouraging outcomes with the caveat of severe long-term toxicities. The last decade has unleashed a revolution in terms of evolved understanding of this heterogeneous disease entity in terms of molecular biology. Medulloblastoma as of today is grouped into one of four canonical molecular subgroups (WNT, SHH, Group 3, and Group 4) each characterized by different putative cells of origin, characteristic aberrations at the molecular level, radiogenomics, and outcomes. Our understanding continues to grow in this regard. The future promises much in terms of personalized medicine in tailoring therapy to the needs of individual patients based on their clinical and molecular profile in order to maximize individual and population based outcomes at the cost of minimizing toxicity.

Pearls and Pitfalls of Imaging in Pediatric Brain Tumors

The central nervous system (CNS) tumors constitute the most common type of solid tumors in the pediatric population. The cerebral and cerebellar parenchyma are the most common site of pediatric CNS neoplasms. Imaging plays an important role in detection, characterization, staging and prognostication of brain tumors. The focus of the current article is pediatric brain tumor imaging with emphasis on pearls and pitfalls of conventional and advanced imaging in various pediatric brain tumor subtypes. The article also elucidates changes in brain tumor terms and entities as applicable to pediatric patients, updated as per World Health Organization (WHO) 2016 classification of primary CNS tumors. This classification introduced the genetic and/or molecular information of primary CNS neoplasms as part of comprehensive tumor pathology report in the routine clinical workflow. The concepts from 2016 classification have been further refined based on current research, by the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) group and published in the form of updates. The updates serve as guidelines in the time interval between WHO updates and expect to be broadly adopted in the subsequent WHO classification. The current review covers most pediatric brain tumors except pituitary tumors, meningeal origin tumors, nerve sheath tumors and CNS lymphoma/leukemia.

Case Report: Primary Leptomeningeal Medulloblastoma in a Child: Clinical Case Report and Literature Review

Medulloblastoma is one of the most common pediatric central nervous system malignancies worldwide, and it is characterized by frequent leptomeningeal metastasizing. We report a rare case of primary leptomeningeal medulloblastoma of an 11-year-old Caucasian girl with a long-term disease history, non-specific clinical course, and challenges in the diagnosis verification. To date, 4 cases of pediatric primary leptomeningeal medulloblastoma are reported, and all of them are associated with unfavorable outcomes. The approaches of neuroimaging and diagnosis verification are analyzed in the article to provide opportunities for effective diagnosis of this disease in clinical practice. The reported clinical case of the primary leptomeningeal medulloblastoma is characterized by MR images with non-specific changes in the brain and spinal cord and by 18FDG-PET/CT images with diffuse heterogeneous hyperfixation of the radiopharmaceutical along the whole spinal cord. The immunohistochemistry and next-generation sequencing analyses of tumor samples were performed for comprehensive characterization of the reported clinical case.

Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications

Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.

Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma

BACKGROUND AND PURPOSE

Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes.

MATERIALS AND METHODS

We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features.

RESULTS

From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI.

CONCLUSIONS

Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.

A case of medulloblastoma in a patient with fetal ventricular enlargement

Medulloblastoma is the second-most common malignant tumor in children. Medulloblastoma has been categorized into four distinct molecular subgroups: WNT, sonic hedgehog (SHH), group 3, and group 4. We report on a male child with medulloblastoma, in whom an enlarged ventricle was diagnosed in utero. Magnetic resonance imaging showed cyst formation in the cerebellar hemisphere initially, with tumor growth being indicated later. Tumor resection was performed when the boy was 12 months old. The histological findings showed extensive nodularity. Further genetic analysis revealed the tumor to be SHH type. This is the first description of a medulloblastoma observed from the fetal stage. Our findings in this case indicate that cyst formation may be the pre-neoplastic lesion of SHH-subtype medulloblastomas.

Advanced MRI Findings in Medulloblastomas: Relationship to Genetic Subtypes, Histopathology, and Immunohistochemistry

BACKGROUND AND PURPOSE

For diagnosis of medulloblastoma, the updated World Health Organization classification now demands for genetic typing, defining more precisely the tumor biology, therapy, and prognosis. We investigated potential associations between magnetic resonance imaging (MRI) parameters including apparent diffusion coefficient (ADC) and neuropathologic features of medulloblastoma, focusing on genetic subtypes.

METHODS

This study was a retrospective single-center analysis of 32 patients (eight females, median age = 9 years [range, 1-57], mean 12.6 ± 11.3) from 2012 to 2019. Genetic subtypes (wingless [WNT]; sonic hedgehog [SHH]; non-WNT/non-SHH), histopathology, immunohistochemistry (p53, Ki67), and the following MRI parameters were correlated: tumor volume, location (midline, pontocerebellar, and cerebellar hemisphere), edema, hydrocephalus, metastatic disease (presence/absence and each), contrast-enhancement (minor, moderate, and distinct), cysts (none, small, and large), hemorrhage (none, minor, and major), and ADC_mean . The ADC_mean was calculated using manually set regions of interest within the solid tumor. Statistics comprised univariate and multivariate testing.

RESULTS

Out of 32 tumors, three tumors were WNT activated (9.4%), 13 (40.6%) SHH activated, and 16 (50.0%) non-WNT/non-SHH. Hemispherical location (n = 7/8, P = .003) and presence of edema (8/8; P < .001, specificity 100%, positive predictive value 100%) were significantly associated with SHH activation. The combined parameter "no edema + no metastatic disease + cysts" significantly discriminated WNT-activated from SHH-activated medulloblastoma (P = .036). ADC_mean (10^-6 mm² /s) was 484 for WNT-activated, 566 for SHH-activated, and 624 for non-WNT/non-SHH subtypes (P = .080). A significant negative correlation was found between ADC_mean and Ki67 (r = -.364, P = .040).

CONCLUSION

MRI analysis enabled noninvasive differentiation of SHH-activated medulloblastoma. ADC alone was not reliable for genetic characterization, but associated with tumor proliferation rate.

Molecular subgrouping of medulloblastoma based on few-shot learning of multitasking using conventional MR images: a retrospective multicenter study

Background

The determination of molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4—of medulloblastomas is very important for prognostication and risk-adaptive treatment strategies. Due to the rare disease characteristics of medulloblastoma, we designed a unique multitask framework for the few-shot scenario to achieve noninvasive molecular subgrouping with high accuracy.

Methods

We introduced a multitask technique based on mask regional convolutional neural network (Mask-RCNN). By effectively utilizing the comprehensive information including genotyping, tumor mask, and prognosis, multitask technique, on the one hand, realized multi-purpose modeling and simultaneously, on the other hand, promoted the accuracy of the molecular subgrouping. One hundred and thirteen medulloblastoma cases were collected from 4 hospitals during the 8-year period in the retrospective study, which were divided into 3-fold cross-validation cohorts (N = 74) from 2 hospitals and independent testing cohort (N = 39) from the other 2 hospitals. Comparative experiments of different auxiliary tasks were designed to illustrate the effect of multitasking in molecular subgrouping.

Results

Compared to the single-task framework, the multitask framework that combined 3 tasks increased the average accuracy of molecular subgrouping from 0.84 to 0.93 in cross-validation and from 0.79 to 0.85 in independent testing. The average area under the receiver operating characteristic curves (AUCs) of molecular subgrouping were 0.97 in cross-validation and 0.92 in independent testing. The average AUCs of prognostication also reached to 0.88 in cross-validation and 0.79 in independent testing. The tumor segmentation results achieved the Dice coefficient of 0.90 in both cohorts.

Conclusions

The multitask Mask-RCNN is an effective method for the molecular subgrouping and prognostication of medulloblastomas with high accuracy in few-shot learning.

The clinical treatment and outcome of cerebellopontine angle medulloblastoma: a retrospective study of 15 cases

Medulloblastoma (MB) is the most common malignant pediatric brain tumor arising in the cerebellum or the 4^th ventricle. Cerebellopontine angle (CPA) MBs are extremely rare tumors, with few cases previously described. In this study, we sought to describe the clinical characteristics, molecular features and outcomes of CPA MB. We retrospectively reviewed a total of 968 patients who had a histopathological diagnosis of MB at the Beijing Neurosurgical Institute between 2002 and 2016. The demographic characteristics, clinical manifestations and radiological features were retrospectively analyzed. Molecular subgroup was evaluated by the expression profiling array or immunohistochemistry. Overall survival (OS) and progression-free survival (PFS) were calculated using Kaplan-Meier analysis. In this study, 15 patients (12 adults and 3 children) with a mean age at diagnosis of 25.1 years (range 4–45 years) were included. CPA MBs represented 1.5% of the total cases of MB (15/968). Two molecular subgroups were identified in CPA MBs: 5 WNT-MBs (33%) and 10 SHH-MBs (67%). CPA WNT-MBs had the extracerebellar growth with the involvement of brainstem (P = 0.002), whereas CPA SHH-MBs predominantly located within the cerebellar hemispheres (P = 0.004). The 5-year OS and PFS rates for CPA MB were 80.0% ± 10.3% and 66.7% ± 12.2%, respectively. Pediatric patients with CPA MBs had worse outcomes than adult patients (OS: P = 0.019, PFS: P = 0.078). In conclusion, CPA MB is extremely rare and consists of two subgroups. Adult patients with CPA MB had a good prognosis. Maximum safe surgical resection combined with adjuvant radiotherapy and chemotherapy can be an effective treatment strategy for this rare tumor.

Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients

Background

Novel biological insights have led to consensus classification of medulloblastoma into 4 distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We aimed to predict molecular subgrouping in medulloblastoma based on preoperative multiparametric magnetic resonance imaging (MRI) characteristics.

Methods

A set of 19 MRI features were evaluated in 111 patients with histologic diagnosis of medulloblastoma for prediction of molecular subgrouping. MRI characteristics were correlated with molecular subgroups derived from tissue samples in 111 patients (WNT = 17, SHH = 44, Group 3 = 27, and Group 4 = 23). Multinomial logistic regression of imaging parameters was performed on a training cohort (TC) of 76 patients, representing two-thirds of randomly selected patients from each of 4 molecular subgroups, to generate binary nomograms. Validation of these nomograms was performed on the remaining 35 patients as the validation cohort (VC).

Results

Medulloblastoma subgroups could be accurately predicted by preoperative MRI features in 74% of cases. Predictive accuracy was excellent for SHH (95%), acceptably high for Group 4 (78%), modest for Group 3 (56%) and worst for WNT (41%) subgroup medulloblastoma. SHH-specific nomogram was associated with excellent correlation between TC and VC, with area under the curve (AUC) of 0.939 and 0.991, respectively. AUC for Group 4 was acceptable at 0.851 and 0.788 in TC and VC, respectively; however, these values were consistently suboptimal in WNT and Group 3 medulloblastoma.

Conclusion

The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.

Correlation between MRI characteristics of medulloblastoma with histopathological subtypes and 2-year survival

Objectives:

The objective of this study is to describe the imaging features of medulloblastoma (MB) and correlate the MR characteristics with the different histological subtype of MB with 2-year survival.

Materials and Methods:

This is a retrospective descriptive study. A total of 29 patients diagnosed with MB from January 2005 to December 2015 were included in this study. The MRI brain and spine studies of these patients were retrieved and reviewed by a pediatric radiologist and a neuroradiologist independently, both blinded from the histological type of the MB. The HPE slides were also retrieved and reviewed by a pathologist.

Results:

80% of desmoplastic MB showed the presence of intracranial leptomeningeal seeding and 57.1% of anaplastic MB showed the presence of necrosis. The presence of intracranial leptomeningeal seeding (P = 0.002) and necrosis (P = 0.019) was predictive of the histological subtypes. There is a significant correlation between the enhancement pattern and the 2-year outcome (P = 0.03) with 6 out of 8 patients whose tumors showed minimal enhancement having disease progression within 2 years. A significant correlation was also seen between the presence of necrosis with a poorer outcome (P = 0.03) and between the HPE subtype and 2-year outcome (P = 0.03) with anaplastic MB having the poorest prognosis.

Conclusion:

MR imaging features of intracranial leptomeningeal seeding and the presence of necrosis were correlated with a specific histologic subtype of MB. The enhancement pattern as well as necrosis correlated with 2-year poorer outcome of the disease.

Texture Analysis of T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging Potentially Predicts Outcomes of Patients with Non-Wingless-Type/Non-Sonic Hedgehog Medulloblastoma

OBJECTIVE

To investigate whether tumor texture features derived from preoperative T1-weighted magnetic resonance imaging (MRI) are associated with overall survival (OS) of patients with non-wingless-type (WNT)/non-sonic hedgehog (SHH) medulloblastoma.

METHODS

We retrospectively reviewed 38 patients with non-WNT/non-SHH (encompassing group 3 and group 4) medulloblastoma treated with surgery in our institution from 2013 to 2016. All patients were followed-up for at least 2 years or until death. Primary tumor traditional parameters were evaluated, and texture features were extracted from preoperative T1-weighted MRI, including 4 features from the histogram matrix and 6 textures from the gray-level co-occurrence matrix (GLCM). Texture features were dichotomized into 2 subgroups based on their optimal cutoff values obtained from receiver operating characteristics curve analysis. Two-year OS was compared between the dichotomized subgroups using the Kaplan-Meier analysis and log-rank test. Multivariate Cox regression analysis was performed to determine independent prognostic factors.

RESULTS

The therapy regimen was the only basic characteristic significantly related to 2-year OS (P = 0.015). Two features of the GLCM were shown to be significantly associated with 24-month OS. Multivariate Cox regression analysis revealed that GLCM homogeneity (adjusted hazard ratio, 0.145; P = 0.013) was an independent prognostic predictor for patients.

CONCLUSIONS

Texture analysis on T1-weighted contrast-enhanced MRI potentially serves as a prognostic predictor of survival for patients with non-WNT/non-SHH medulloblastoma.

Pediatric Brain Tumor Genetics: What Radiologists Need to Know

Brain tumors are the most common solid tumors in the pediatric population. Pediatric neuro-oncology has changed tremendously during the past decade owing to ongoing genomic advances. The diagnosis, prognosis, and treatment of pediatric brain tumors are now highly reliant on the genetic profile and histopathologic features of the tumor rather than the histopathologic features alone, which previously were the reference standard. The clinical information expected to be gleaned from radiologic interpretations also has evolved. Imaging is now expected to not only lead to a relevant short differential diagnosis but in certain instances also aid in predicting the specific tumor and subtype and possibly the prognosis. These processes fall under the umbrella of radiogenomics. Therefore, to continue to actively participate in patient care and/or radiogenomic research, it is important that radiologists have a basic understanding of the molecular mechanisms of common pediatric central nervous system tumors. The genetic features of pediatric low-grade gliomas, high-grade gliomas, medulloblastomas, and ependymomas are reviewed; differences between pediatric and adult gliomas are highlighted; and the critical oncogenic pathways of each tumor group are described. The role of the mitogen-activated protein kinase pathway in pediatric low-grade gliomas and of histone mutations as epigenetic regulators in pediatric high-grade gliomas is emphasized. In addition, the oncogenic drivers responsible for medulloblastoma, the classification of ependymomas, and the associated imaging correlations and clinical implications are discussed. ^©RSNA, 2018.

Clinical and magnetic resonance imaging findings of a cerebellar medulloblastoma in a heifer

Reports of medulloblastoma in cattle are scarce; however, this neoplasm should be included as a differential diagnosis in cases of cerebellar or central vestibular signs in young cattle. The MRI appearance of the medulloblastoma reported here, previously unreported in cattle, consisted of a T1-weighted hypointense and T2-weighted heterogeneously hyperintense intra-axial mass.

MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma

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.

Subfrontal recurrence after cerebellar medulloblastoma resection without local relapse: case-based update

OBJECTIVE

This report detailed four cases of tumor recurrence in the subfrontal region after cerebellar medulloblastoma resection without local relapse and explored the causes of recurrence. In addition, a case-based update and insight into the entity is attempted.

METHODS

All four patients received cerebellar medulloblastoma resection and postoperative radiotherapy. They were admitted to our hospital when they were found to have a recurrent tumor in the subfrontal region of the anterior skull base. All four patients received re-resection of the tumor, which was confirmed to be recurrent medulloblastoma by postoperative pathological results.

RESULTS

All patients received local radiotherapy and temozolomide chemotherapy after recurrent tumor resection. They all died due to multiple organ failure resulting from tumor metastasis to other sites or tumor regrowth within 2 years after the second operation.

CONCLUSION

Medulloblastoma metastasize to the subfrontal region and develop a homogenous recurrence is rare. Underdosage of radiation, a gravity-related sanctuary effect, surgical position, and perioperative hydrocephalus management might be factors contributing to this supratentorial meningeal recurrence. A better prevention of tumor recurrence might be achieved by extensive microsurgical tumor resection in the initial operation and by minimizing the need for a permanent V-P shunt in the treatment of perioperative hydrocephalus as well as by administering full-dose radiotherapy to the region of the cribriform plate in the subfrontal area.

Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma

BACKGROUND

Medulloblastomas are the most common central nervous system tumors in childhood. Treatment and prognosis strongly depend on histology and transcriptomic profiling. However, the proliferative potential also has prognostical value. Our study aimed to investigate correlations between histogram profiling of diffusion-weighted images and further microarchitectural features.

MATERIAL AND METHODS

Seven patients (age median 14.6 years, minimum 2 years, maximum 20 years; 5 male, 2 female) were included in this retrospective study. Using a Matlab-based analysis tool, histogram analysis of whole apparent diffusion coefficient (ADC) volumes was performed.

RESULTS

ADC entropy revealed a strong inverse correlation with the expression of the proliferation marker Ki67 (r = - 0.962, p = 0.009) and with total nuclear area (r = - 0.888, p = 0.044). Furthermore, ADC percentiles, most of all ADCp90, showed significant correlations with Ki67 expression (r = 0.902, p = 0.036).

DISCUSSION AND CONCLUSION

Diffusion histogram profiling of medulloblastomas provides valuable in vivo information which potentially can be used for risk stratification and prognostication. First of all, entropy revealed to be the most promising imaging biomarker. However, further studies are warranted.

Embryonal Tumors of the Central Nervous System: From the Radiologic Pathology Archives

Embryonal tumors of the central nervous system (CNS) are highly malignant undifferentiated or poorly differentiated tumors of neuroepithelial origin and have been defined as a category in the World Health Organization (WHO) classification since the first edition of the "Blue Book" in 1979. This category has evolved over time to reflect our ever-improving understanding of tumor biology and behavior. With the most recent update in 2016, many previous histologic diagnoses incorporate molecular parameters for the first time (genetically defined entities). While medulloblastoma and atypical teratoid/rhabdoid tumor are familiar carryovers from the 2007 CNS WHO classification, there are major changes to the embryonal tumor category: for example, elimination of the term CNS primitive neuroectodermal tumor and addition of a new genetically defined entity, embryonal tumor with multilayered rosettes, C19MC-altered. The purpose of this article is to discuss both the radiologic-pathologic features of CNS embryonal tumors and the new molecularly defined types/subtypes that will become the standard classification/terminology for future diagnoses and tumor research.

Imaging features of medulloblastoma: Conventional imaging, diffusion-weighted imaging, perfusion-weighted imaging, and spectroscopy: From general features to subtypes and characteristics

Medulloblastoma is a frequent high-grade neoplasm among pediatric brain tumours. Its classical imaging features are a midline tumour growing into the fourth ventricle, hyperdense on CT-scan, displaying a hypersignal when using diffusion-weighted imaging, with a variable contrast enhancement. Nevertheless, atypical imaging features have been widely reported, varying according to the age of the patient, and histopathological subtype. In this study, we review the classical and atypical imaging features of medulloblastomas, with emphasis on advanced MRI techniques, histopathological and molecular subtypes and characteristics, and follow-up modalities.

MRI features as a helpful tool to predict the molecular subgroups of medulloblastoma: state of the art

Medulloblastoma is the most common malignant pediatric brain tumor. Medulloblastoma should not be viewed as a single disease, but as a heterogeneous mixture of various subgroups with distinct characteristics. Based on genomic profiles, four distinct molecular subgroups are identified: Wingless (WNT), Sonic Hedgehog (SHH), Group 3 and Group 4. Each of these subgroups are associated with specific genetic aberrations, typical age of onset as well as survival prognosis. Magnetic resonance imaging (MRI) is performed for all patients with brain tumors, and has a key role in the diagnosis, surgical guidance and follow up of patients with medulloblastoma. Several studies indicate MRI as a promising tool for early detection of medulloblastoma subgroups. The early identification of the subgroup can influence the extent of surgical resection, radiotherapy and chemotherapy targeted treatments. In this article, we review the state of the art in MRI-facilitated medulloblastoma subgrouping, with a summary of the main MRI features in medulloblastoma and a brief discussion on molecular characterization of medulloblastoma subgroups. The main focus of the article is MRI features that correlate with medulloblastoma subtypes, as well as features suggestive of molecular subgroups. Finally, we briefly discuss the latest trends in MRI studies and latest developments in molecular characterization.

MRI Characteristics of Primary Tumors and Metastatic Lesions in Molecular Subgroups of Pediatric Medulloblastoma: A Single-Center Study

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.

Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging

Background

Imaging diagnosis of medulloblastoma recurrence relies heavily on identifying new contrast-enhancing lesions on surveillance imaging, with diffusion-weighted imaging (DWI) being used primarily for detection of complications. We propose that DWI is more sensitive in detecting distal and leptomeningeal recurrent medulloblastoma than T1-weighted postgadolinium imaging.

Methods

We identified 53 pediatric patients with medulloblastoma, 21 of whom developed definitive disease recurrence within the brain. MRI at diagnosis of recurrence and 6 months prior was evaluated for new lesions with reduced diffusion on DWI, contrast enhancement, size, and recurrence location.

Results

All recurrent medulloblastoma lesions demonstrated reduced diffusion. Apparent diffusion coefficient (ADC) measurements were statistically significantly lower (P = .00001) in recurrent lesions (mean=0.658, SD=0.072) as compared to contralateral normal region of interest (mean=0.923, SD=0.146). Sixteen patients (76.2%) with disease recurrence demonstrated contrast enhancement within the recurrent lesions. All 5 patients with nonenhancing recurrence demonstrated reduced diffusion, with a mean ADC of 0.695 ± 0.101 (normal=0.893 ± 0.100, P = .0027). While group 3 and group 4 molecular subtypes demonstrated distal recurrence more frequently, nonenhancing metastatic disease was found in all molecular subtypes.

Conclusion

Recurrent medulloblastoma lesions do not uniformly demonstrate contrast enhancement on MRI, but all demonstrate reduced diffusion. Our findings support that DWI is more sensitive than contrast enhancement for detection of medulloblastoma recurrence, particularly in cases of leptomeningeal nonenhancing disease and distal nonenhancing focal disease. As such, recurrent medulloblastoma can present as a reduced diffusion lesion in a patient with normal postgadolinium contrast MRI.

Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade

BACKGROUND AND PURPOSE

Accurate tumor grading is essential for treatment planning of pediatric brain tumors. We hypothesized that multiparametric analyses of a combination of permeability metrics and ADC histogram metrics would differentiate high- and low-grade tumors with high accuracy.

MATERIALS AND METHODS

DTI and dynamic contrast-enhanced MR imaging using T1-mapping with flip angles of 2°, 5°, 10°, and 15°, followed by a 0.1-mmol/kg body weight gadolinium-based bolus was performed on all patients in addition to standard MR imaging. Permeability data were processed and transfer constant from the blood plasma into the extracellular extravascular space, rate constant from the extracellular extravascular space back into blood plasma, extravascular extracellular volume fraction, and fractional blood plasma volume were calculated from 3D tumor volumes. Apparent diffusion coefficient histogram metrics were calculated for 3 separate tumor volumes derived from T2-FLAIR sequences, T1 contrast-enhanced sequences, and permeability maps, respectively.

RESULTS

Results from 41 patients (0.3-16.76 years of age; mean, 6.22 years) with newly diagnosed contrast-enhancing brain tumors (16 low-grade; 25 high-grade) were included in the institutional review board-approved retrospective analysis. Wilcoxon tests showed a higher transfer constant from blood plasma into extracellular extravascular space and rate constant from extracellular extravascular space back into blood plasma, and lower extracellular extravascular volume fraction (P < .001) in high-grade tumors. The mean ADCs of FLAIR and enhancing tumor volumes were significantly lower in high-grade tumors (P < .001). ROC analysis showed that a combination of extravascular volume fraction and mean ADC of FLAIR volume differentiated high- and low-grade tumors with high accuracy (area under receiver operating characteristic curve = 0.918).

CONCLUSIONS

ADC histogram metrics combined with permeability metrics differentiate low- and high-grade pediatric brain tumors with high accuracy.

Imaging Biomarkers for Adult Medulloblastomas: Genetic Entities May Be Identified by Their MR Imaging Radiophenotype

BACKGROUND AND PURPOSE

The occurrence of medulloblastomas in adults is rare; nevertheless, these tumors can be subdivided into genetic and histologic entities each having distinct prognoses. This study aimed to identify MR imaging biomarkers to classify these entities and to uncover differences in MR imaging biomarkers identified in pediatric medulloblastomas.

MATERIALS AND METHODS

Eligible preoperative MRIs from 28 patients (11 women; 22-53 years of age) of the Multicenter Pilot-study for the Therapy of Medulloblastoma of Adults (NOA-7) cohort were assessed by 3 experienced neuroradiologists. Lesions and perifocal edema were volumetrized and multiparametrically evaluated for classic morphologic characteristics, location, hydrocephalus, and Chang criteria. To identify MR imaging biomarkers, we correlated genetic entities sonic hedgehog (SHH) TP53 wild type, wingless (WNT), and non-WNT/non-SHH medulloblastomas (in adults, Group 4), and histologic entities were correlated with the imaging criteria. These MR imaging biomarkers were compared with corresponding data from a pediatric study.

RESULTS

There were 19 SHH TP53 wild type (69%), 4 WNT-activated (14%), and 5 Group 4 (17%) medulloblastomas. Six potential MR imaging biomarkers were identified, 3 of which, hydrocephalus (P = .03), intraventricular macrometastases (P = .02), and hemorrhage (P = .04), when combined, could identify WNT medulloblastoma with 100% sensitivity and 88.3% specificity (95% CI, 39.8%-100.0% and 62.6%-95.3%). WNT-activated nuclear β-catenin accumulating medulloblastomas were smaller than the other entities (95% CI, 5.2-22.3 cm³ versus 35.1-47.6 cm³; P = .03). Hemorrhage was exclusively present in non-WNT/non-SHH medulloblastomas (P = .04; n = 2/5). MR imaging biomarkers were all discordant from those identified in the pediatric cohort. Desmoplastic/nodular medulloblastomas were more rarely in contact with the fourth ventricle (4/15 versus 7/13; P = .04).

CONCLUSIONS

MR imaging biomarkers can help distinguish histologic and genetic medulloblastoma entities in adults and appear to be different from those identified in children.

Distinctive localization and MRI features correlate of molecular subgroups in adult medulloblastoma

Medulloblastoma (MB) is recognized as comprising four molecular subgroups with distinct transcriptional profiles, clinical features, and outcomes. Previous studies demonstrate that pediatric MBs present with subgroup-specific MRI manifestations. We hypothesized that combination of anatomical localization and conventional features based on MR imaging can predict these subgroups in adult MBs. MR Imaging manifestations of 125 adult patients with MB were analyzed retrospectively based on pre-operative MRI scans. MB molecular subgroups were evaluated by the expression profiling array and immunohistochemistry. A pediatric MB cohort of 60 patients were analyzed for comparison with data of adult patients. Multiple logistic regression analysis revealed that tumor location (P < 0.0001) and pattern of enhancement (P = 0.0048) were significantly correlated with molecular subgroups in adult MBs. Ninety-two percent of adult MBs were correctly predicted by using logistic regression model based on the anatomical localization patterns and pattern of enhancement. Exclusively intra-cerebellar growth, localization in the rostral cerebellum, and no brainstem contact were specific to adult SHH-MBs. Group 4-MBs in adult were characterized by minimal/no enhancement compared with other two subgroups. Infant SHH-MBs represented significant different localization patterns compared with SHH tumors in children and adults. We identified that molecular subgroups of adult MBs could be well predicted by tumor localization patterns and enhancement pattern. Our study also provided important evidence that MB subgroups in adult possibly derived from different cellular origins.

Case-based review: pediatric medulloblastoma

Medulloblastoma is the most common malignant brain tumor affecting children. These tumors are high grade with propensity to metastasize within the central nervous system and, less frequently, outside the neuraxis. Recent advancements in molecular subgrouping of medulloblastoma refine diagnosis and improve counseling in regards to overall prognosis. Both are predicated on the molecular drivers of each subgroup-WNT-activated, SHH-activated, group 3, and group 4. The traditional therapeutic mainstay for medulloblastoma includes a multimodal approach with surgery, radiation, and multiagent chemotherapy. As we discover more about the molecular basis of medulloblastoma, efforts to adjust treatment approaches based on molecular risk stratification are under active investigation. Certainly, the known neurological, developmental, endocrine, and psychosocial injury related to medulloblastoma and its associated therapies motivate ongoing research towards improving treatment for this life-threatening tumor while at the same time minimizing long-term side effects.

Added value of diffusion weighted imaging in pediatric central nervous system embryonal tumors surveillance

Diffusion weighted imaging (DWI) has an established role in primary CNS embryonal tumor (ET) characterization; however, its diagnostic utility in detecting relapse has never been determined. We aimed to compare DWI and conventional MRI sensitivity in CNS ET recurrence detection, and to evaluate the DWI properties of contrast-enhancing radiation induced lesions (RIL). Fifty-six patients with CNS ET (25 with disease relapse, 6 with RIL and 25 with neither disease relapse nor RIL) were retrospectively evaluated with DWI, conventional MRI (including both T2/FLAIR and post-contrast images), or contrast-enhanced MR imaging (CE-MRI) alone. MRI studies were independently reviewed by two neuroradiologists for detection and localization of potential brain relapses. Sensitivity for focal relapse detection was calculated for each image set on a lesion-by-lesion basis. A descriptive per subject analysis was also performed. Evaluation of follow-up MRI studies served as standard of reference. Focal recurrence detection sensitivity of DWI (96%) was significantly higher than conventional MRI (77%) and CE-MRI alone (51%) (p=0.0003 and p<0.0001). On per subject analysis there were not missed diagnoses for DWI. At the time of DWI relapse detection, conventional MRI missed 2 diagnoses, and CE-MRI 8. Analysis of medulloblastoma relapses revealed that DWI identified a higher number of focal lesions than CE-MRI in subjects with classic variant. All but one RIL did not show restricted diffusion. In conclusion, DWI is a valuable complementary technique allowing for improved detection of focal relapse in CNS ET patients, particularly in children with classic medulloblastoma, and may assist in differentiating recurrence from RIL.

Magnetic resonance imaging based functional imaging in paediatric oncology

Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.

Anomalous vascularization in a Wnt medulloblastoma: a case report

BACKGROUND

Medulloblastoma is the most common malignant brain tumor in children. To date only few cases of medulloblastoma with hemorrhages have been reported in the literature. Although some studies speculate on the pathogenesis of this anomalous increased vascularization in medulloblastoma, the specific mechanism is still far from clearly understood. A correlation between molecular medulloblastoma subgroups and hemorrhagic features has not been reported, although recent preliminary studies described that WNT-subtype tumors display increased vascularization and hemorrhaging.

CASE PRESENTATION

Herein, we describe a child with a Wnt-medulloblastoma presenting as cerebellar-vermian hemorrhagic lesion. Brain magnetic resonance imaging (MRI) showed the presence of a midline posterior fossa mass with a cystic hemorrhagic component. The differential diagnosis based on imaging included cavernous hemangioma, arteriovenous malformation and traumatic lesion. At surgery, the tumor appeared richly vascularized as documented by the preoperative angiography.

CONCLUSIONS

The case we present showed that Wnt medulloblastoma may be associated with anomalous vascularization. Further studies are needed to elucidate if there is a link between the hypervascularization and the Wnt/β-catenin signaling activation and if this abnormal vasculature might influence drug penetration contributing to good prognosis of this medulloblastoma subgroup.