Pediatric Brain Tumor Genetics: What Radiologists Need to Know

Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning

BACKGROUND AND PURPOSE

Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molecular status of PLGNTs.

MATERIALS AND METHODS

In this retrospective bi-institutional study, we searched the PACS for baseline brain MRIs from children with PLGNTs. Semiautomated tumor segmentation on ADC maps was performed using the semiautomated level tracing effect tool with 3D Slicer. Clinical variables, including age, sex, and tumor location, were collected from chart review. The molecular status of tumors was derived from biopsy. Multiclass random forests were used to predict the molecular status and fine-tuned using a grid search on the validation sets. Models were evaluated using independent and unseen test sets based on the combined data, and the area under the receiver operating characteristic curve (AUC) was calculated for the prediction of 3 classes: KIAA1549-BRAF fusion, BRAF V600E mutation, and non-BRAF cohorts. Experiments were repeated 100 times using different random data splits and model initializations to ensure reproducible results.

RESULTS

Two hundred ninety-nine children from the first institution and 23 children from the second institution were included (53.6% male; mean, age 8.01 years; 51.8% supratentorial; 52.2% with KIAA1549-BRAF fusion). For the 3-class prediction using radiomics features only, the average test AUC was 0.74 (95% CI, 0.73-0.75), and using clinical features only, the average test AUC was 0.67 (95% CI, 0.66-0.68). The combination of both radiomics and clinical features improved the AUC to 0.77 (95% CI, 0.75-0.77). The diagnostic performance of the per-class test AUC was higher in identifying KIAA1549-BRAF fusion tumors among the other subgroups (AUC = 0.81 for the combined radiomics and clinical features versus 0.75 and 0.74 for BRAF V600E mutation and non-BRAF, respectively).

CONCLUSIONS

ADC values of tumor segmentations have differentiative signals that can be used for training machine learning classifiers for molecular biomarker identification of PLGNTs. ADC-based pretherapeutic differentiation of the BRAF status of PLGNTs has the potential to avoid invasive tumor biopsy and enable earlier initiation of targeted therapy.

Imaging features to distinguish posterior fossa ependymoma subgroups

OBJECTIVES

Posterior fossa ependymoma group A (EPN_PFA) and group B (EPN_PFB) can be distinguished by their DNA methylation and give rise to different prognoses. We compared the MRI characteristics of EPN_PFA and EPN_PFB at presentation.

METHODS

Preoperative imaging of 68 patients with posterior fossa ependymoma from two centers was reviewed by three independent readers, blinded for histomolecular grouping. Location, tumor extension, tumor volume, hydrocephalus, calcifications, tissue component, enhancement or diffusion signal, and histopathological data (cellular density, calcifications, necrosis, mitoses, vascularization, and microvascular proliferation) were compared between the groups. Categorical data were compared between groups using Fisher's exact tests, and quantitative data using Mann-Whitney tests. We performed a Benjamini-Hochberg correction of the p values to account for multiple tests.

RESULTS

Fifty-six patients were categorized as EPN_PFA and 12 as EPN_PFB, with median ages of 2 and 20 years, respectively (p = 0.0008). The median EPN_PFA tumoral volume was larger (57 vs 29 cm3, p = 0.003), with more pronounced hydrocephalus (p = 0.002). EPN_PFA showed an exclusive central position within the 4th ventricle in 61% of patients vs 92% for EPN_PFB (p = 0.01). Intratumor calcifications were found in 93% of EPN_PFA vs 40% of EPN_PFB (p = 0.001). Invasion of the posterior fossa foramina was mostly found for EPN_PFA, particularly the foramina of Luschka (p = 0.0008). EPN_PFA showed whole and homogeneous tumor enhancement in 5% vs 75% of EPN_PFB (p = 0.0008). All mainly cystic tumors were EPN_PFB (p = 0.002). The minimal and maximal relative ADC was slightly lower in EPN_PFA (p = 0.02 and p = 0.01, respectively).

CONCLUSION

Morphological characteristics from imaging differ between posterior fossa ependymoma subtypes and may help to distinguish them preoperatively.

CLINICAL RELEVANCE STATEMENT

This study provides a tool to differentiate between group A and group B ependymomas, which will ultimately allow the therapeutic strategy to be adapted in the early stages of patient management.

KEY POINTS

• Posterior fossa ependymoma subtypes often have different imaging characteristics. • Posterior fossa ependymomas group A are commonly median or lateral tissular calcified masses, with incomplete enhancement, affecting young children and responsible for pronounced hydrocephalus and invasion of the posterior fossa foramina. • Posterior fossa ependymomas group B are commonly median non-calcified masses of adolescents and adults, predominantly cystic, and minimally invasive, with total and homogeneous enhancement.

Glioma: bridging the tumor microenvironment, patient immune profiles and novel personalized immunotherapy

Glioma is the most common primary brain tumor, characterized by a consistently high patient mortality rate and a dismal prognosis affecting both survival and quality of life. Substantial evidence underscores the vital role of the immune system in eradicating tumors effectively and preventing metastasis, underscoring the importance of cancer immunotherapy which could potentially address the challenges in glioma therapy. Although glioma immunotherapies have shown promise in preclinical and early-phase clinical trials, they face specific limitations and challenges that have hindered their success in further phase III trials. Resistance to therapy has been a major challenge across many experimental approaches, and as of now, no immunotherapies have been approved. In addition, there are several other limitations facing glioma immunotherapy in clinical trials, such as high intra- and inter-tumoral heterogeneity, an inherently immunosuppressive microenvironment, the unique tissue-specific interactions between the central nervous system and the peripheral immune system, the existence of the blood-brain barrier, which is a physical barrier to drug delivery, and the immunosuppressive effects of standard therapy. Therefore, in this review, we delve into several challenges that need to be addressed to achieve boosted immunotherapy against gliomas. First, we discuss the hurdles posed by the glioma microenvironment, particularly its primary cellular inhabitants, in particular tumor-associated microglia and macrophages (TAMs), and myeloid cells, which represent a significant barrier to effective immunotherapy. Here we emphasize the impact of inducing immunogenic cell death (ICD) on the migration of Th17 cells into the tumor microenvironment, converting it into an immunologically "hot" environment and enhancing the effectiveness of ongoing immunotherapy. Next, we address the challenge associated with the accurate identification and characterization of the primary immune profiles of gliomas, and their implications for patient prognosis, which can facilitate the selection of personalized treatment regimens and predict the patient's response to immunotherapy. Finally, we explore a prospective approach to developing highly personalized vaccination strategies against gliomas, based on the search for patient-specific neoantigens. All the pertinent challenges discussed in this review will serve as a compass for future developments in immunotherapeutic strategies against gliomas, paving the way for upcoming preclinical and clinical research endeavors.

The dark side of T2: central nervous system lesions with low signal intensity on T2-weighted imaging

The majority of central nervous system diseases show high signal intensity on T2-weighted magnetic resonance imaging. Diseases of the central nervous system with low signal intensity are less common, which makes it a finding that helps narrow the differential diagnosis. This was a retrospective analysis of brain and spine magnetic resonance imaging examinations in which that finding was helpful in the diagnostic investigation. We selected the cases of patients examined between 2015 and 2022. All diagnoses were confirmed on the basis of the clinical-radiological correlation or the histopathological findings. We obtained images of 14 patients with the following central nervous system diseases: arteriovenous malformation; cavernous malformation; metastasis from lymphoma; medulloblastoma; embryonal tumor; metastasis from melanoma; Rathke's cleft cyst; Erdheim-Chester disease; aspergillosis; paracoccidioidomycosis; tuberculosis; syphilis; immunoglobulin G4-related disease; and metastasis from a pulmonary neuroendocrine tumor. We described lesions of different etiologies in which the T2-weighted imaging profile helped narrow the differential diagnosis and facilitated the definitive diagnosis.

MRI characteristics predict BRAF V600E status in gangliogliomas and pleomorphic xanthoastrocytomas and provide survival prognostication

BACKGROUND

BRAF V600E mutation is a common genomic alteration in gangliogliomas (GGs) and pleomorphic xanthoastrocytomas (PXAs) with prognostic and therapeutic implications.

PURPOSE

To investigate the ability of magnetic resonance imaging (MRI) features to predict BRAF V600E status in GGs and PXAs and their prognostic values.

MATERIAL AND METHODS

A cohort of 44 patients with histologically confirmed GGs and PXAs was reviewed retrospectively. BRAF V600E status was determined by immunohistochemistry (IHC) staining and fluorescence quantitative polymerase chain reaction (PCR). Demographics and MRI characteristics of the two groups were evaluated and compared. Univariate and multivariate Cox regression analyses were performed to identify MRI features that were prognostic for progression-free survival (PFS).

RESULTS

T1/FLAIR ratio, enhancing margin, and mean relative apparent diffusion coefficient (rADCmea) value showed significant differences between the BRAF V600E-mutant and BRAF V600E-wild groups (all P < 0.05). Binary logistic regression analysis revealed only rADCmea value was the independent predictive factor for BRAF V600E status (P = 0.027). Univariate Cox regression analysis showed age at diagnosis (P = 0.032), WHO grade (P = 0.020), enhancing margin (P = 0.029), and rADCmea value (P = 0.005) were significant prognostic factors for PFS. In multivariate Cox regression analysis, increasing age (P = 0.040, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.002-1.079) and lower rADCmea values (P = 0.021, HR = 0.036, 95% CI = 0.002-0.602) were associated with poor PFS in GGs and PXAs.

CONCLUSION

Imaging features are potentially predictive of BRAF V600E status in GGs and PXAs. Furthermore, rADCmea value is a valuable prognostic factor for patients with GGs or PXAs.

Posterior fossa tumors in children: current insights

While in adults most intracranial tumors develop around the cerebral hemispheres, 45 to 60% of pediatric lesions are found in the posterior fossa, although this anatomical region represents only 10% of the intracranial volume. The latest edition of the WHO classification for CNS tumors presented some fundamental paradigm shifts that particularly affected the classification of pediatric tumors, also influencing those that affect posterior fossa. Molecular biomarkers play an important role in the diagnosis, prognosis, and treatment of childhood posterior fossa tumors and can be used to predict patient outcomes and response to treatment and monitor its effectiveness. Although genetic studies have identified several posterior fossa tumor types, differing in terms of their location, cell of origin, genetic mechanisms, and clinical behavior, recent management strategies still depend on uniform approaches, mainly based on the extent of resection. However, significant progress has been made in guiding therapy decisions with biological or molecular stratification criteria and utilizing molecularly targeted treatments that address specific tumor biological characteristics. The primary focus of this review is on the latest advances in the diagnosis and treatment of common subtypes of posterior fossa tumors in children, as well as potential therapeutic approaches in the future.   Conclusion: Molecular biomarkers play a central role, not only in the diagnosis and prognosis of posterior fossa tumors in children but also in customizing treatment plans. They anticipate patient outcomes, measure treatment responses, and assess therapeutic effectiveness. Advances in neuroimaging and treatment have significantly enhanced outcomes for children with these tumors. What is Known: • Central nervous system tumors are the most common solid neoplasms in children and adolescents, with approximately 45 to 60% of them located in the posterior fossa. • Multimodal approaches that include neurosurgery, radiation therapy, and chemotherapy are typically used to manage childhood posterior fossa tumors What is New: • Notable progress has been achieved in the diagnosis, categorization and management of posterior fossa tumors in children, leading to improvement in survival and quality of life.

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

BACKGROUND

Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.

OBJECTIVE AND METHODS

This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.

RESULTS

A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.

CONCLUSION

Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

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.

Prognostic Values of Core Genes in Pilocytic Astrocytom

BACKGROUND

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis.

METHODS

The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves.

RESULTS

Compared with normal brain tissues (n = 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n = 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio  =  2.8, P  <  0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio =  3.2, P  <  0.01).

CONCLUSIONS

This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Current role of machine learning and radiogenomics in precision neuro-oncology

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.

T2-FLAIR Mismatch Sign in Pediatric Low-Grade Glioma

BACKGROUND AND PURPOSE

No qualitative imaging feature currently predicts molecular alterations of pediatric low-grade gliomas with high sensitivity or specificity. The T2-FLAIR mismatch sign predicts IDH-mutated 1p19q noncodeleted adult gliomas with high specificity. We aimed to assess the significance of the T2-FLAIR mismatch sign in pediatric low-grade gliomas.

MATERIALS AND METHODS

Pretreatment MR images acquired between January 2001 and August 2018 in pediatric patients with pediatric low-grade gliomas were retrospectively identified. Inclusion criteria were the following: 1) 0-18 years of age, 2) availability of molecular information in histopathologically confirmed cases, and 3) availability of preoperative brain MR imaging with non-motion-degraded T2-weighted and FLAIR sequences. Spinal cord tumors were excluded.

RESULTS

Three hundred forty-nine patients were included (187 boys; mean age, 8.7 [SD, 4.8] years; range, 0.5-17.7 years). KIAA1549-B-Raf proto-oncogene (BRAF) fusion and BRAF p.V600E mutation were the most common molecular markers (n = 148, 42%, and n = 73, 20.7%, respectively). The T2-FLAIR mismatch sign was present in 25 patients (7.2%). Of these, 9 were dysembryoplastic neuroepithelial tumors; 8, low-grade astrocytomas; 5, diffuse astrocytomas; 1, a pilocytic astrocytoma; 1, a glioneuronal tumor; and 1, an angiocentric glioma. None of the 25 T2-FLAIR mismatch pediatric low-grade gliomas were BRAF p.V600E-mutated. Fourteen of 25 pediatric low-grade gliomas with the T2-FLAIR mismatch sign had rare molecular alterations, while the molecular subtype was unknown for 11 tumors.

CONCLUSIONS

The T2-FLAIR mismatch sign was not observed in the common molecular alterations, BRAF p.V600E-mutated and KIAA1549-BRAF fused pediatric low-grade gliomas, while it was encountered in pediatric low-grade gliomas with rare pediatric molecular alterations.

[Pediatric posterior fossa tumors]

CLINICAL ISSUE

Tumors of the posterior fossa account for about 50-55% of brain tumors in childhood.

DIAGNOSTIC WORKUP

The most frequent tumor entities are medulloblastomas, pilocytic astrocytomas, ependymomas, diffuse midline gliomas and atypical teratoid-rhabdoid tumors. Neuroradiological differential diagnosis with magnetic resonance imaging (MRI) is of considerable importance for preoperative planning as well as planning of follow-up therapy.

PERFORMANCE

Most important findings for differential diagnosis of pediatric posterior fossa tumors are tumor location, patient age and the intratumoral apparent diffusion assessed by diffusion-weighted imaging.

ACHIEVEMENTS

Advanced MR techniques like MRI perfusion and MR spectroscopy can be helpful both in the initial differential diagnosis and in tumor surveillance, but exceptional characteristics of certain tumor entities should be kept in mind.

PRACTICAL RECOMMENDATIONS

Standard clinical MRI sequences including diffusion-weighted imaging are the main diagnostic tool in evaluating posterior fossa tumors in children. Advanced imaging methods can be helpful, but should never be interpreted separately from conventional MRI sequences.

Posterior fossa tumors in children: An update and new concepts

Background:

Posterior fossa tumors account for approximately half of the central nervous system tumors in children. Major technological advances, mainly in the fields of molecular biology and neuroimaging, have modified their classification, leading to a more detailed description of these entities. Into the classic taxonomy, used for many years, new concepts have been incorporated at times eliminating or modifying former ones.

Methods:

A literature search was conducted in PubMed using the medical subject headings involving the five most common pediatric posterior fossa tumors: diffuse midline glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor, and pilocytic astrocytoma. Only English published articles in the past 11 years that provided technological, neuroimaging, and molecular biology insight into posterior fossa tumors in children were considered.

Results:

Substantial changes have been introduced in the nomenclature of pediatric posterior fossa tumors. Diffuse midline gliomas are named based on alterations in histone H3. Molecular rearrangements of medulloblastomas are more important in defining the prognosis than histological variants; therefore, these tumors are currently named based on their molecular subgroups. Posterior fossa ependymomas and atypical teratoid rhabdoid tumor classification have incorporated new groups based on different genetic profiles. Pilocytic astrocytoma has been placed in a new category that distinguishes circumscribed from diffuse entities.

Conclusion:

Advances in molecular biology and neuroimaging have substantially changed the way pediatric neoplasms are studied. The classical taxonomy has been modified leading to more accurate classifications that are based on the genetic alterations.

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

INTRODUCTION

Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes.

METHODS

Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes.

RESULTS

K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes.

CONCLUSION

In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.

The Role of Neuro-Inflammation and Innate Immunity in Pathophysiology of Brain and Spinal Cord Tumors

Inflammation and innate immune system play a central role in cancers, including those affecting the central nervous system (CNS). Currently, classification of neoplasms, especially regarding gliomas, is established on molecular mutations in isocitrate dehydrogenase (IDH) genes and the presence of co-deletion 1p/19q. Treatment, in most of brain and spinal cord tumors, is centered on surgery, radiotherapy and pharmacological approaches with chemotherapeutic agents. However, the results of the treatments, after several decades, are not completely satisfactory. Cytokines and angiogenic factors are closely linked to the brain cancer behavior. Moreover, recent studies suggest a link between inflammation and tumorigenesis, underlying the complex nature of this topic, especially the anti- and pro-tumoral activities of inflammation and the two-way interactions between immune and tumor cells. The current understanding of the mechanisms by which CNS cancer cells modulate the immune system, especially how bi-directional communications between immune cells and tumor cells create an immunosuppressed microenvironment, gives important information about the promotion of tumor survival and growth. Here, we have briefly reviewed the current literature on this topic, focusing on the possible role of inflammation and innate immunity involved in the origin and in the development of CNS tumors.

A case report of pilocytic astrocytoma mimicking meningioma on imaging

Pilocytic astrocytoma (PA) is categorized as grade I gliomas with a good prognosis. Although PA mostly occurs in the cerebellum, it also can occur in the orbital and mostly presents as a cystic tumor with a mural nodule. PA often presents in the second decade of life, with 75% occurring under the age of 20 years. This case report describes a 10-year-old boy presented a left eye tumor for over 3 years. MRI examination showed unrestricted intraconal lesions in the optic nerve, visible from the optic canal to the anterior with well-defined borders. The excised tumor specimen depicted a nodular tumor tissue, measuring 35 × 28 × 20 mm, weighing 11 grams, solid with gray and white. The microscopic examination showed a classical biphasic pattern including combinations of loose glial tissue and compact pyloid tissue. Hispathology result revealed a pilocytic astrocytoma.

Posterior fossa ependymoma H3 K27-mutant: an integrated radiological and histomolecular tumor analysis

Posterior fossa group A ependymomas (EPN_PFA) are characterized by a loss of H3 K27 trimethylation due to either EZHIP overexpression or H3 p.K27M mutation, similar to H3 K27-altered diffuse midline gliomas (DMG), but in reverse proportions. Very little data is available in the literature concerning H3 K27M-mutant EPN_PFA. Here, we retrospectively studied a series of nine pediatric tumors initially diagnosed as H3 K27M-mutant EPN_PFA to compare them to EZHIP-overexpressing EPN_PFA in terms of radiology, follow-up, histopathology, and molecular biology (including DNA-methylation profiling). Seven tumors clustered within EPN_PFA by DNA-methylation analysis and t-distributed stochastic neighbor embedding. Among the two remaining cases, one was reclassified as a DMG and the last was unclassified. H3 K27M-mutant EPN_PFA cases were significantly older than their counterparts with an EZHIP overexpression. Radiological and histopathological central review of our seven H3 K27M-mutant EPN_PFA cases found them to be similar to their counterparts with an EZHIP overexpression. Sequencing analyses revealed HIST1H3B (n = 2), HIST1H3C (n = 2), H3F3A (n = 1), and HIST1H3D (n = 1) K27M mutations (no sequencing analysis available for the last case which was immunopositive for H3K27M). Consequently, HIST1H3C/D mutations are more frequently observed in EPN_PFA than in classic pontine DMG, H3K27-mutant. Overall survival and event-free survival of EZHIP-overexpressing and H3 K27M-mutant EPN_PFA were similar. After surgery and radiation therapy, 5/7 patients were alive at the end of the follow-up. In summary, the diagnosis of EPN_PFA must include tumor location, growth pattern, Olig2 expression, and DNA-methylation profiling before it can be differentiated from DMG, H3 K27-altered.

MR Imaging Characteristics and ADC Histogram Metrics for Differentiating Molecular Subgroups of Pediatric Low-Grade Gliomas

BACKGROUND AND PURPOSE

BRAF and type 1 neurofibromatosis status are distinctive features in pediatric low-grade gliomas with prognostic and therapeutic implications. We hypothesized that DWI metrics obtained through volumetric ADC histogram analyses of pediatric low-grade gliomas at baseline would enable early detection of BRAF and type 1 neurofibromatosis status.

MATERIALS AND METHODS

We retrospectively evaluated 40 pediatric patients with histologically proved pilocytic astrocytoma (n = 33), ganglioglioma (n = 4), pleomorphic xanthoastrocytoma (n = 2), and diffuse astrocytoma grade 2 (n = 1). Apart from 1 patient with type 1 neurofibromatosis who had a biopsy, 11 patients with type 1 neurofibromatosis underwent conventional MR imaging to diagnose a low-grade tumor without a biopsy. BRAF molecular analysis was performed for patients without type 1 neurofibromatosis. Eleven patients presented with BRAF V600E-mutant, 20 had BRAF-KIAA rearrangement, and 8 had BRAF wild-type tumors. Imaging studies were reviewed for location, margins, hemorrhage or calcifications, cystic components, and contrast enhancement. Histogram analysis of tumoral diffusivity was performed.

RESULTS

Diffusion histogram metrics (mean, median, and 10th and 90th percentiles) but not kurtosis or skewness were different among pediatric low-grade glioma subgroups (P < .05). Diffusivity was lowest in BRAF V600E-mutant tumors (the 10th percentile reached an area under the curve of 0.9 on receiver operating characteristic analysis). There were significant differences between evaluated pediatric low-grade glioma margins and cystic components (P = .03 and P = .001, respectively). Well-defined margins were characteristic of BRAF-KIAA or wild-type BRAF rather than BRAF V600E-mutant or type 1 neurofibromatosis tumors. None of the type 1 neurofibromatosis tumors showed a cystic component.

CONCLUSIONS

Imaging features of pediatric low-grade gliomas, including quantitative diffusion metrics, may assist in predicting BRAF and type 1 neurofibromatosis status, suggesting a radiologic-genetic correlation, and might enable early genetic signature characterization.

Zebrafish Models of Paediatric Brain Tumours

Paediatric brain cancer is the second most common childhood cancer and is the leading cause of cancer-related deaths in children. Despite significant advancements in the treatment modalities and improvements in the 5-year survival rate, it leaves long-term therapy-associated side effects in paediatric patients. Addressing these impairments demands further understanding of the molecularity and heterogeneity of these brain tumours, which can be demonstrated using different animal models of paediatric brain cancer. Here we review the use of zebrafish as potential in vivo models for paediatric brain tumour modelling, as well as catalogue the currently available zebrafish models used to study paediatric brain cancer pathophysiology, and discuss key findings, the unique attributes that these models add, current challenges and therapeutic significance.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

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.

Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics

Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.

MR Imaging of Pediatric Brain Tumors

Primary brain tumors are the most common solid neoplasms in children and a leading cause of mortality in this population. MRI plays a central role in the diagnosis, characterization, treatment planning, and disease surveillance of intracranial tumors. The purpose of this review is to provide an overview of imaging methodology, including conventional and advanced MRI techniques, and illustrate the MRI appearances of common pediatric brain tumors.

Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas

BACKGROUND AND PURPOSE

Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types.

MATERIALS AND METHODS

Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio.

RESULTS

The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively.

CONCLUSIONS

In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.

A Holistic Review on the Current and Future Status of Biology-Driven and Broad-Spectrum Therapeutic Options for Medulloblastoma

With a thorough investigation of the etiology of medulloblastomas, a comprehensive review was done to categorize available clinical trials in order to discuss the future potential of breakthroughs in treatment options. The pertinent issues of medulloblastoma therapy with radiation being inapplicable to children under the age of 3, and therapies causing toxicity are detailed and discussed in the context of understanding how the current therapies may address these suboptimal treatment modalities. This study aggregated published studies from the US government clinical trials website and filtered them based on their direct treatment towards medulloblastomas. Thirty-two clinical trials were applicable to be analyzed and the treatment mechanisms were discussed along with the efficacy; molecular groupings of medulloblastomas were also investigated. The investigated therapies tend to target sonic hedgehog (SHH)-subtype medulloblastomas, but there is a necessity for group 3 subtype and group 4 subtype to be targeted as well. Due to the heterogeneous nature of tumor relapse in groups 3 and 4, there are less specified trials towards those molecular groupings, and radiation seems to be the main scope of treatment. Medulloblastomas being primarily a pediatric tumor require treatment options that minimize radiation to increase the quality of living in children and to prevent long-term symptoms of over radiation. Exploring symptomatic treatment with donepezil in children with combination therapies may be a potential route for future trials; immunotherapies seem to hold potential in treating patients reacting adversely to radiation therapy.

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.

The role of artificial intelligence in paediatric neuroradiology

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

Imaging Features with Histopathologic Correlation of CNS High-Grade Neuroepithelial Tumors with a BCOR Internal Tandem Duplication

BACKGROUND AND PURPOSE

A new brain tumor entity occurring in early childhood characterized by a somatic BCL6 corepressor gene internal tandem duplication was recently described. The aim of this study was to describe the radiologic pattern of these tumors and correlate this pattern with histopathologic findings.

MATERIALS AND METHODS

This retrospective, noninterventional study included 10 children diagnosed with a CNS tumor, either by ribonucleic acid-sequencing analysis or deoxyribonucleic acid methylation analysis. Clinical, radiologic, and histopathologic data were collected. A neuropathologist reviewed 9 tumor samples. Preoperative images were analyzed in consensus by 7 pediatric radiologists.

RESULTS

All tumors were relatively large (range, 4.7-9.2 cm) intra-axial peripheral masses with well-defined borders and no peritumoral edema. All tumors showed mild and heterogeneous enhancement and marked restriction on DWI of the solid portions. Perfusion imaging showed a relatively lower CBF in the tumor than in the adjacent normal parenchyma. Nine of 10 tumors showed areas of necrosis, with the presence of hemorrhage in 8/10 and calcifications in 4/7. Large intratumoral macroscopic veins were observed in 9/10 patients. No intracranial or spinal leptomeningeal dissemination was noted at diagnosis.

CONCLUSIONS

CNS tumors with a BCL6 corepressor gene internal tandem duplication present as large intra-axial peripheral masses with well-defined borders, no edema, restricted diffusion, weak contrast enhancement, frequent central necrosis, hemorrhage and calcifications, intratumoral veins, and no leptomeningeal dissemination at the time of diagnosis. Knowledge of these imaging characteristics may aid in histologic, genomic, and molecular profiling of brain tumors in young children.

Texture analysis imaging "what a clinical radiologist needs to know"

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.

T-Cell Immunotherapy for Pediatric High-Grade Gliomas: New Insights to Overcoming Therapeutic Challenges

Despite decades of research, pediatric central nervous system (CNS) tumors remain the most debilitating, difficult to treat, and deadliest cancers. Current therapies, including radiation, chemotherapy, and/or surgery, are unable to cure these diseases and are associated with serious adverse effects and long-term impairments. Immunotherapy using chimeric antigen receptor (CAR) T cells has the potential to elucidate therapeutic antitumor immune responses that improve survival without the devastating adverse effects associated with other therapies. Yet, despite the outstanding performance of CAR T cells against hematologic malignancies, they have shown little success targeting brain tumors. This lack of efficacy is due to a scarcity of targetable antigens, interactions with the immune microenvironment, and physical and biological barriers limiting the homing and trafficking of CAR T cells to brain tumors. In this review, we summarize experiences with CAR T-cell therapy for pediatric CNS tumors in preclinical and clinical settings and focus on the current roadblocks and novel strategies to potentially overcome those therapeutic challenges.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Updates on the 2016 World Health Organization Classification of Pediatric Tumors of the Central Nervous System - a systematic review

Tumors of the central nervous system (CNS) represent the main cause of death through solid tumors in children and the second most frequent neoplasm in this patient group. The poor survival rate is due to many factors, such as the large diversity of morphological features, the particular micro-environmental characteristics of the nervous tissue, the relative rareness in relation to other childhood diseases, which leads to late diagnosis and the limited effectiveness of the available treatment options. Up until 2016, brain tumors were classified according to their histologic features. The new 2016 World Health Organization (WHO) Classification of CNS tumors incorporates molecular features, alongside the immunohistology, in order to provide a more accurate understanding of the disease. The treatment consists of surgery, radiation therapy and chemotherapy. We decided to review the literature on this pathology, in order to show the importance of the recent discoveries in this field.

Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era

Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.

Trametinib for orbital plexiform neurofibromas in young children with neurofibromatosis type 1

INTRODUCTION

Plexiform neurofibromas (PNF) in neurofibromatosis type 1 (NF1) are usually diagnosed in childhood and can grow rapidly during this period. In 10% of patients, PNF involve the orbital-periorbital area and may cause visual problems including glaucoma, visual loss from amblyopia (deprivational, strabismic, or refractive), optic nerve compression, or keratopathy. Ptosis, proptosis, and facial disfigurement lead to social problems and decreased self-esteem. Complete surgical removal involves significant risks and mutilation, and regrowth after debulking is not uncommon. Inhibitors of the RAS/MAPK pathway have recently been investigated for their activity in PNF. We administered the oral MEK inhibitor trametinib to five young children with NF1 and PNF of the orbital area, with visual compromise and progressive tumor growth; and followed them clinically and by volumetric MRI.

METHODS

Treatment was initiated at a mean age of 26.8 months (SD ± 12.8) and continued for a median 28 months (range 16-51). Doses were 0.025 mg/kg/day for children aged > 6 years and 0.032 mg/kg/day for those aged < 6 years.

RESULTS

Volumetric MRI measurements showed a reduction of 2.9-33% at 1 year after treatment initiation, with maximal reductions of 44% and 49% in two patients, at 44 and 36 months, respectively. No change in visual function was recorded during treatment. One child reported decreased orbital pain after 2 weeks; and another, with involvement of the masseters, had increased ability to chew food. Toxicities were mostly to skin and nails, grades 1-2.

CONCLUSIONS

Trametinib can decrease tumor size in some young children with orbital PNF and may prevent progressive disfigurement.

Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

Genomic studies have provided insights into molecular subgroups and oncogenic drivers of pediatric brain tumors (PBT) that may lead to novel therapeutic strategies. Participants of the cohort Pediatric Brain Tumor Atlas: CBTTC (CBTTC cohort), were randomly divided into training and validation cohorts. In the training cohort, Kaplan-Meier analysis and univariate Cox regression model were applied to preliminary screening of prognostic genes. The LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation and CBTTC cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. Also, gene set enrichment analysis (GSEA) and immune infiltrating analyses were conducted to understand function annotation and the role of the signature in the tumor microenvironment. An eight-gene signature was built, which was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen, either in the training or validation cohorts. The eight-gene signature was further proven to be independent of other clinic-pathologic parameters via the Cox regression analyses. Moreover, ROC analysis demonstrated that this signature owned a better predictive power of PBT prognosis. Furthermore, GSEA and immune infiltrating analyses showed that the signature had close interactions with immune-related pathways and was closely related to CD8 T cells and monocytes in the tumor environment. Identifying the eight-gene signature (CBX7, JADE2, IGF2BP3, OR2W6P, PRAME, TICRR, KIF4A, and PIMREG) could accurately identify patients' prognosis and the signature had close interactions with the immunodominant tumor environment, which may provide insight into personalized prognosis prediction and new therapies for PBT patients.

Imaging of brain tumors in children: the basics-a narrative review

Primary pediatric brain tumors comprise a broad group of neoplasm subtypes that can be categorized based on their histological and molecular features according to the 2016 World Health Organization (WHO) classification of central nervous system (CNS) tumors. The majority of the pediatric brain tumors demonstrate a singular preference for this age group and have a unique molecular profile. The separation of certain tumor entities, including different types of embryonal tumors, low-grade gliomas, and high-grade gliomas, may have a significant impact by guiding appropriate treatment for these children and potentially changing their outcomes. Currently, the focus of the imaging diagnostic studies is to follow the molecular updates, searching for potential imaging patterns that translate this information in molecular profile results, therefore helping the final diagnosis. Due to the high impact of accurate diagnosis in this context, the scientific community has presented extensive research on imaging pediatric tumors in recent years. This article summarizes the key characteristics of the imaging features of the most common primary childhood brain tumors, categorizing them according to the recent WHO classification update, which is based on each of their molecular profiles. The purpose of this review article is to familiarize radiologists with their key imaging features and thereby improve diagnostic accuracy.

Diffusion histology imaging differentiates distinct pediatric brain tumor histology

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

Imaging pitfalls in paediatric posterior fossa neoplastic and non-neoplastic lesions

Paediatric posterior fossa lesions can have much overlap in their clinical and radiological presentation. There are, however, a number of key imaging features that can help the reading radiologist to distinguish tumours from important tumour mimics which are often inflammatory or metabolic entities. This pictorial review provides a number of important cases that proved challenging on imaging and illustrates some common pitfalls when interpreting lesions in the posterior fossa in children. Not everything that is abnormal will be a tumour, but often other causes are overlooked and misinterpreted as tumours, leading to great morbidity for that child. This article highlights some lesions that were mistaken as tumours and will introduce the reader to less commonly seen pathologies which are important to consider on a differential list for this location.

Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors

BACKGROUND AND PURPOSE

B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation.

MATERIALS AND METHODS

In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance.

RESULTS

The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96).

CONCLUSIONS

Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.

Radiology Profile as a Potential Instrument to Differentiate Between Posterior Fossa Ependymoma (PF-EPN) Group A and B

OBJECTIVE

Posterior fossa ependymoma (PF-EPN) was categorized into PF-EPN-A and PF-EPN-B subgroups based on the DNA methylation profiling. PF-EPN-A was reported to have poorer prognosis compared with PF-EPN-B. In this study, we particularly evaluated preoperative imaging to distinguish PF-EPN-A from PF-EPN-B.

METHODS

Sixteen cases of PF-EPN were treated in our institution from 1999 to 2018. The patients were divided into PF-EPN-A and PF-EPN-B groups based on H3K27me3 immunostaining positivity. We evaluated progression-free survival, overall survival, as well as preoperative magnetic resonance imaging and computed tomography scan images in both groups. Based on T1WI and Gd-T1WI magnetic resonance images, the tumor contrast rate was determined from dividing the volume of gadolinium enhanced tumor by the overall tumor volume.

RESULTS

Nine cases (4 male, 5 female) were grouped as PF-EPN-A, and 7 (4 male, 3 female) as PF-EPN-B. The median age of PF-EPN-A and PF-EPN-B were 4 and 43 years old, respectively. In the PF-EPN-A group, the progression-free survival median value was 32.6 months, and the overall survival median was 96.9 months. In contrast, PFS in PF-EPN-B did not reach a median value (P < 0.05) and all the patients were alive (P < 0.05) at the end of the study. With imaging, tumor contrast rate in PF-EPN-B was more than 50% and significantly different from PF-EPN-A (P = 0.0294). Calcification was mainly observed in PF-EPN-A, whereas cystic formation was only seen in PF-EPN-B.

CONCLUSIONS

Contrast rate less than 50%, based on the magnetic resonance images, was characteristic in the PF-EPN-A group. Comparatively, cystic component and absence of calcification were more characteristic in the PF-EPN-B group.

Polymorphous low-grade neuroepithelial tumor of the young: case report and review focus on the radiological features and genetic alterations

BACKGROUND

A new type of epileptogenic tumor, the polymorphous low-grade neuroepithelial tumor of the young (PLNTY) was firstly reported by Jason T. Huse et al. at 2016. After that, only 1 case of PLNTY was reported by article. The radiological characteristics of PLNTY have not been concluded. The objective of our study was to report 3 cases of PLNTYs in details and to analyze the image characteristics and genetic alterations of PLNTYs by reviewing our cases and articles.

CASE PRESENTATION

There were 3 cases diagnosed as PLNTY by pathology in our hospital during the last 10 years, with the average age of 15. They were all suffered from different degrees of epilepsy. All of them underwent magnetic resonance (MR) imaging and 2 of them underwent computer tomography (CT) imaging. The PLNTYs are all appearing as a solid or solid-cystic cortical mass with little mass effect and unclear boundary with normal brain tissue. They are all shown as hyperintensity in T2WI and iso-/hypointensity in T1WI with slight or no enhancement after contract enhanced in MR imaging. The "salt and pepper sign" in T2WI and grit calcification in CT images might be specific characteristics of PLNTY. All of them recovered after excision of the tumors. The gene tests revealed fibroblast growth factor receptors 3 (FGFR3)-TACC3 fusion and FGFR3 amplification in one case, and the B-Raf proto-oncogene (BRAF) V600E mutation in another case.

CONCLUSION

In the image, the partial ill-marginated cortical mass with "salt and pepper sign" in T2WI or grit calcification in CT imaging might be the typical imaging characteristics of PLNTY. We also prove that the BRAF V600E mutation as well as the FGFR2 and FGFR3 have a close relationship with PLNTY.

Role of diffusion weighted imaging for differentiating cerebral pilocytic astrocytoma and ganglioglioma BRAF V600E-mutant from wild type

PURPOSE

BRAF V600E mutation is a distinctive genomic alteration of pediatric low-grade gliomas with prognostic and therapeutic implications. The aim of this retrospective multicenter study was to analyze imaging features of BRAF V600E-mutant and wild-type cerebral pilocytic astrocytomas (PAs) and gangliogliomas (GGs), focusing on the role of diffusion weighted imaging (DWI).

METHODS

We retrospectively evaluated 56 pediatric patients with histologically proven, treatment-naïve PAs and GGs who underwent conventional MRI, DWI, and molecular analysis for BRAF V600E mutation. Twenty-three subjects presented BRAF V600E-mutant (12 PAs and 11 GGs) and 33 BRAF V600E wild-type (26 PAs and 7 GGs) tumors. Imaging studies were reviewed for dominant site, margin definition, hemorrhage, calcification, cystic components, contrast enhancement, and relative mean and minimum ADC values (rADCmean and rADCmin). Statistics included Fisher's exact test, Student t test, general linear model, and receiver operating characteristic (ROC) analysis.

RESULTS

PA and GG BRAF V600E-mutant had significantly lower rADCmean (p < 0.001) and rADCmin (p < 0.001) values than wild type, regardless of tumor histology and location. ROC analysis demonstrated similar performances between these parameters in predicting BRAF V600E status (rADCmean: AUC 0.831, p < 0.001; rADCmin: AUC 0.885, p < 0.001). No significant differences regarding additional imaging features emerged between BRAF V600E-mutant and wild-type lesions, with the exception of the number of tumors with cystic components, significantly higher in BRAF V600E-mutant PAs (p = 0.011) CONCLUSION: Assessment of the DWI characteristics of GGs and PAs may assist in predicting BRAF V600E status, suggesting a radiogenomic correlation and prompt molecular characterization of these tumors.