Morphological and ultrastructural investigation of the posterior atlanto-occipital membrane: Comparing children with Chiari malformation type I and controls.

INTRODUCTION: The fibrous posterior atlanto-occipital membrane (PAOM) at the craniocervical junction is typically removed during decompression surgery for Chiari malformation type I (CM-I); however, its importance and ultrastructural architecture have not been investigated in children. We hypothesized that there are structural differences in the PAOM of patients with CM-I and those without. METHODS: In this prospective study, blinded pathological analysis was performed on PAOM specimens from children who had surgery for CM-I and children who had surgery for posterior fossa tumors (controls). Clinical and radiographic data were collected. Statistical analysis included comparisons between the CM-I and control cohorts and correlations with imaging measures. RESULTS: A total of 35 children (mean age at surgery 10.7 years; 94.3% white) with viable specimens for evaluation were enrolled: 24 with CM-I and 11 controls. There were no statistical demographic differences between the two cohorts. Four children had a family history of CM-I and five had a syndromic condition. The cohorts had similar measurements of tonsillar descent, syringomyelia, basion to C2, and condylar-to-C2 vertical axis (all p>0.05). The clival-axial angle was lower in patients with CM-I (138.1 vs. 149.3 degrees, p = 0.016). Morphologically, the PAOM demonstrated statistically higher proportions of disorganized architecture in patients with CM-I (75.0% vs. 36.4%, p = 0.012). There were no differences in PAOM fat, elastin, or collagen percentages overall and no differences in imaging or ultrastructural findings between male and female patients. Posterior fossa volume was lower in children with CM-I (163,234 mm3 vs. 218,305 mm3, p<0.001), a difference that persisted after normalizing for patient height (129.9 vs. 160.9, p = 0.028). CONCLUSIONS: In patients with CM-I, the PAOM demonstrates disorganized architecture compared with that of control patients. This likely represents an anatomic adaptation in the presence of CM-I rather than a pathologic contribution.

Diaphragm Pacing: A Safety, Appropriateness, Financial Neutrality, and Efficacy Analysis of Treating Chronic Respiratory Insufficiency.

OBJECTIVES: This study aimed to evaluate the safety and applicability of treating chronic respiratory insufficiency with diaphragm pacing relative to mechanical ventilation. MATERIALS AND METHODS: A literature review and analysis were conducted using the safety, appropriateness, financial neutrality, and efficacy principles. RESULTS: Although mechanical ventilation is clearly indicated in acute respiratory failure, diaphragm pacing improves life expectancy, increases quality of life, and reduces complications in patients with chronic respiratory insufficiency. CONCLUSION: Diaphragm pacing should be given more consideration in appropriately selected patients with chronic respiratory insufficiency.

Tacedinaline (CI-994), a class I HDAC inhibitor, targets intrinsic tumor growth and leptomeningeal dissemination in MYC-driven medulloblastoma while making them susceptible to anti-CD47-induced macrophage phagocytosis via NF-kB-TGM2 driven tumor inflammation.

BACKGROUND: While major advances have been made in improving the quality of life and survival of children with most forms of medulloblastoma (MB), those with MYC-driven tumors (Grp3-MB) still suffer significant morbidity and mortality. There is an urgent need to explore multimodal therapeutic regimens which are effective and safe for children. Large-scale studies have revealed abnormal cancer epigenomes caused by mutations and structural alterations of chromatin modifiers, aberrant DNA methylation, and histone modification signatures. Therefore, targeting epigenetic modifiers for cancer treatment has gained increasing interest, and inhibitors for various epigenetic modulators have been intensively studied in clinical trials. Here, we report a cross-entity, epigenetic drug screen to evaluate therapeutic vulnerabilities in MYC amplified MB, which sensitizes them to macrophage-mediated phagocytosis by targeting the CD47-signal regulatory protein α (SIRPα) innate checkpoint pathway. METHODS: We performed a primary screen including 78 epigenetic inhibitors and a secondary screen including 20 histone deacetylase inhibitors (HDACi) to compare response profiles in atypical teratoid/rhabdoid tumor (AT/RT, n=11), MB (n=14), and glioblastoma (n=14). This unbiased approach revealed the preferential activity of HDACi in MYC-driven MB. Importantly, the class I selective HDACi, CI-994, showed significant cell viability reduction mediated by induction of apoptosis in MYC-driven MB, with little-to-no activity in non-MYC-driven MB, AT/RT, and glioblastoma in vitro. We tested the combinatorial effect of targeting class I HDACs and the CD47-SIRPa phagocytosis checkpoint pathway using in vitro phagocytosis assays and in vivo orthotopic xenograft models. RESULTS: CI-994 displayed antitumoral effects at the primary site and the metastatic compartment in two orthotopic mouse models of MYC-driven MB. Furthermore, RNA sequencing revealed nuclear factor-kB (NF-κB) pathway induction as a response to CI-994 treatment, followed by transglutaminase 2 (TGM2) expression, which enhanced inflammatory cytokine secretion. We further show interferon-γ release and cell surface expression of engulfment ('eat-me') signals (such as calreticulin). Finally, combining CI-994 treatment with an anti-CD47 mAb targeting the CD47-SIRPα phagocytosis checkpoint enhanced in vitro phagocytosis and survival in tumor-bearing mice. CONCLUSION: Together, these findings suggest a dynamic relationship between MYC amplification and innate immune suppression in MYC amplified MB and support further investigation of phagocytosis modulation as a strategy to enhance cancer immunotherapy responses.

A likely HOXC4 predisposition variant for Chiari malformations.

OBJECTIVE: Inherited variants predisposing patients to type 1 or 1.5 Chiari malformation (CM) have been hypothesized but have proven difficult to confirm. The authors used a unique high-risk pedigree population resource and approach to identify rare candidate variants that likely predispose individuals to CM and protein structure prediction tools to identify pathogenicity mechanisms. METHODS: By using the Utah Population Database, the authors identified pedigrees with significantly increased numbers of members with CM diagnosis. From a separate DNA biorepository of 451 samples from CM patients and families, 32 CM patients belonging to 1 or more of 24 high-risk Chiari pedigrees were identified. Two high-risk pedigrees had 3 CM-affected relatives, and 22 pedigrees had 2 CM-affected relatives. To identify rare candidate predisposition gene variants, whole-exome sequence data from these 32 CM patients belonging to 24 CM-affected related pairs from high-risk pedigrees were analyzed. The I-TASSER package for protein structure prediction was used to predict the structures of both the wild-type and mutant proteins found here. RESULTS: Sequence analysis of the 24 affected relative pairs identified 38 rare candidate Chiari predisposition gene variants that were shared by at least 1 CM-affected pair from a high-risk pedigree. The authors found a candidate variant in HOXC4 that was shared by 2 CM-affected patients in 2 independent pedigrees. All 4 of these CM cases, 2 in each pedigree, exhibited a specific craniocervical bony phenotype defined by a clivoaxial angle less than 125°. The protein structure prediction results suggested that the mutation considered here may reduce the binding affinity of HOXC4 to DNA. CONCLUSIONS: Analysis of unique and powerful Utah genetic resources allowed identification of 38 strong candidate CM predisposition gene variants. These variants should be pursued in independent populations. One of the candidates, a rare HOXC4 variant, was identified in 2 high-risk CM pedigrees, with this variant possibly predisposing patients to a Chiari phenotype with craniocervical kyphosis.

Treatment of a symptomatic thalamic pilocytic astrocytoma with reservoir placement and laser interstitial thermal therapy: illustrative case.

BACKGROUND: Treatment of pilocytic astrocytomas (PAs) in children can be challenging when they arise in deep midline structures because complete surgical resection may result in significant neurological injury. Laser interstitial thermal therapy (LITT) has provided an alternative treatment modality for lesions that may not be amenable to resection. However, many patients with PAs may be symptomatic from a compressive cyst associated with the PA, and LITT does not obviate the need for cystic decompression in these patients. OBSERVATIONS: A 12-year-old male presented with left-sided weakness. Magnetic resonance imaging (MRI) revealed an enhancing mass with a large cyst involving the right thalamus and basal ganglia. The patient underwent a reservoir placement for cyst drainage and biopsy of the mass, revealing a pilocytic astrocytoma. He then underwent LITT followed by adjuvant chemotherapy. Sixteen months after LITT, follow-up MRI of the brain revealed no tumor growth. LESSONS: This is the first case to describe reservoir placement to treat the cystic portion of a pilocytic astrocytoma followed by LITT and targeted chemotherapy. Reservoir placement reduced the cyst's mass effect and resolved the patient's symptoms, allowing for treatment options beyond resection.

Targeted Next-Generation Sequencing Reveals Divergent Clonal Evolution in Components of Composite Pleomorphic Xanthoastrocytoma-Ganglioglioma.

Composite pleomorphic xanthoastrocytoma-ganglioglioma (PXA-GG) is an extremely rare central nervous system neoplasm with 2 distinct but intermingled components. Whether this tumor represents a "collision tumor" of separate neoplasms or a monoclonal neoplasm with divergent evolution is poorly understood. Clinicopathologic studies and capture-based next generation sequencing were performed on extracted DNA from all available PXA-GG at 2 medical centers. Five PXA-GG were diagnosed in 1 male and 4 female patients ranging from 13 to 25 years in age. Four arose within the cerebral hemispheres; 1 presented in the cerebellar vermis. DNA was sufficient for analysis in 4 PXA components and 3 GG components. Four paired PXA and GG components harbored BRAF p.V600E hotspot mutations. The 4 sequenced PXA components demonstrated CDKN2A homozygous deletion by sequencing with loss of p16 (protein product of CDKN2A) expression by immunohistochemistry, which was intact in all assessed GG components. The PXA components also demonstrated more frequent copy number alterations relative to paired GG components. In one PXA-GG, shared chromosomal copy number alterations were identified in both components. Our findings support divergent evolution of the PXA and GG components from a common BRAF p.V600E-mutant precursor lesion, with additional acquisition of CDKN2A homozygous deletion in the PXA component as is typically seen in conventional PXA.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

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

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

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

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

BACKGROUND: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. METHODS: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. RESULTS: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). CONCLUSIONS: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Developmentally anomalous cerebellar encephalocele arising within the cerebellopontine angle and extending into the adjacent skull base in a pediatric patient.

Lesions of the cerebellopontine angle (CPA) in young children are rare, with the most common being arachnoid cysts and epidermoid inclusion cysts. The authors report a case of an encephalocele containing heterotopic cerebellar tissue arising from the right middle cerebellar peduncle and filling the right internal acoustic canal in a 2-year-old female patient. Her initial presentation included a focal left 6th nerve palsy. Magnetic resonance imaging was suggestive of a high-grade tumor of the right CPA. The lesion was removed via a retrosigmoid approach, and histopathologic analysis revealed heterotopic atrophic cerebellar tissue. This report is the first description of a heterotopic cerebellar encephalocele within the CPA and temporal skull base of a pediatric patient.

Multiple Intraspinal Gangliogliomas in a Child With Neurofibromatosis Type 1: Case Report and Literature Review.

Neurofibromatosis type 1 (NF1)-associated primary intramedullary spinal cord ganglioglioma has only rarely been reported. Because of frequent nonresectability, they pose significant management challenges despite clinical indolence. This report describes a 4-year-old girl with NF1 who was found to have multiple discrete, infiltrative intramedullary cord masses, and biopsy demonstrated World Health Organization grade I ganglioglioma. Panel-based next-generation sequencing showed her previously identified germline NF1 mutation and a second somatic NF1 mutation. This represents the first report of multiple primary intramedullary gangliogliomas in a child with NF1 and demonstrates how biopsy with panel-based next-generation sequencing provides potential targets for MAPK/MEK/BRAF pathway inhibitor therapy.

Semaphorin 3A mediated brain tumor stem cell proliferation and invasion in EGFRviii mutant gliomas.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults, with a median survival of approximately 15 months. Semaphorin 3A (Sema3A), known for its axon guidance and antiangiogenic properties, has been implicated in GBM growth. We hypothesized that Sema3A directly inhibits brain tumor stem cell (BTSC) proliferation and drives invasion via Neuropilin 1 (Nrp1) and Plexin A1 (PlxnA1) receptors. METHODS: GBM BTSC cell lines were assayed by immunostaining and PCR for levels of Semaphorin 3A (Sema3A) and its receptors Nrp1 and PlxnA1. Quantitative BrdU, cell cycle and propidium iodide labeling assays were performed following exogenous Sema3A treatment. Quantitative functional 2-D and 3-D invasion assays along with shRNA lentiviral knockdown of Nrp1 and PlxnA1 are also shown. In vivo flank studies comparing tumor growth of knockdown versus control BTSCs were performed. Statistics were performed using GraphPad Prism v7. RESULTS: Immunostaining and PCR analysis revealed that BTSCs highly express Sema3A and its receptors Nrp1 and PlxnA1, with expression of Nrp1 in the CD133 positive BTSCs, and absence in differentiated tumor cells. Treatment with exogenous Sema3A in quantitative BrdU, cell cycle, and propidium iodide labeling assays demonstrated that Sema3A significantly inhibited BTSC proliferation without inducing cell death. Quantitative functional 2-D and 3-D invasion assays showed that treatment with Sema3A resulted in increased invasion. Using shRNA lentiviruses, knockdown of either NRP1 or PlxnA1 receptors abrogated Sema3A antiproliferative and pro-invasive effects. Interestingly, loss of the receptors mimicked Sema3A effects, inhibiting BTSC proliferation and driving invasion. Furthermore, in vivo studies comparing tumor growth of knockdown and control infected BTSCs implanted into the flanks of nude mice confirmed the decrease in proliferation with receptor KD. CONCLUSIONS: These findings demonstrate the importance of Sema3A signaling in GBM BTSC proliferation and invasion, and its potential as a therapeutic target.

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. METHODS: The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. RESULTS: Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). CONCLUSIONS: The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

Deep Learning for Automated Delineation of Pediatric Cerebral Arteries on Pre-operative Brain Magnetic Resonance Imaging.

Introduction: Surgical resection of brain tumors is often limited by adjacent critical structures such as blood vessels. Current intraoperative navigations systems are limited; most are based on two-dimensional (2D) guidance systems that require manual segmentation of any regions of interest (ROI; eloquent structures to avoid or tumor to resect). They additionally require time- and labor-intensive processing for any reconstruction steps. We aimed to develop a deep learning model for real-time fully automated segmentation of the intracranial vessels on preoperative non-angiogram imaging sequences. Methods: We identified 48 pediatric patients (10-months to 22-years old) with high resolution (0.5-1 mm axial thickness) isovolumetric, pre-operative T2 magnetic resonance images (MRIs). Twenty-eight patients had anatomically normal brains, and 20 patients had tumors or other lesions near the skull base. Manually segmented intracranial vessels (internal carotid, middle cerebral, anterior cerebral, posterior cerebral, and basilar arteries) served as ground truth labels. Patients were divided into 80/5/15% training/validation/testing sets. A modified 2-D Unet convolutional neural network (CNN) architecture implemented with 5 layers was trained to maximize the Dice coefficient, a measure of the correct overlap between the predicted vessels and ground truth labels. Results: The model was able to delineate the intracranial vessels in a held-out test set of normal and tumor MRIs with an overall Dice coefficient of 0.75. While manual segmentation took 1-2 h per patient, model prediction took, on average, 8.3 s per patient. Conclusions: We present a deep learning model that can rapidly and automatically identify the intracranial vessels on pre-operative MRIs in patients with normal vascular anatomy and in patients with intracranial lesions. The methodology developed can be translated to other critical brain structures. This study will serve as a foundation for automated high-resolution ROI segmentation for three-dimensional (3D) modeling and integration into an augmented reality navigation platform.