The clinical and genetic spectrum of inherited glycosylphosphatidylinositol deficiency disorders.

Inherited glycosylphosphatidylinositol deficiency disorders (IGDs) are a group of rare multisystem disorders arising from pathogenic variants in glycosylphosphatidylinositol anchor pathway (GPI-AP) genes. Despite associating 24 of at least 31 GPI-AP genes with human neurogenetic disease, prior reports are limited to single genes without consideration of the GPI-AP as a whole and with limited natural history data. In this multinational retrospective observational study, we systematically analyse the molecular spectrum, phenotypic characteristics and natural history of 83 individuals from 75 unique families with IGDs, including 70 newly reported individuals; the largest single cohort to date. Core clinical features were developmental delay or intellectual disability (DD/ID, 90%), seizures (83%), hypotonia (72%) and motor symptoms (64%). Prognostic and biologically significant neuroimaging features included cerebral atrophy (75%), cerebellar atrophy (60%), callosal anomalies (57%) and symmetric restricted diffusion of the central tegmental tracts (60%). Sixty-one individuals had multisystem involvement including gastrointestinal (66%), cardiac (19%) and renal (14%) anomalies. Though dysmorphic features were appreciated in 82%, no single dysmorphic feature had a prevalence >30%, indicating substantial phenotypic heterogeneity. Follow-up data were available for all individuals, 15 of whom were deceased at the time of writing. Median age at seizure onset was 6 months. Individuals with variants in synthesis stage genes of the GPI-AP exhibited a significantly shorter time to seizure onset than individuals with variants in transamidase and remodelling stage genes of the GPI-AP (P = 0.046). Forty individuals had intractable epilepsy. The majority of individuals experienced delayed or absent speech (95%), motor delay with non-ambulance (64%), and severe-to-profound DD/ID (59%). Individuals with a developmental epileptic encephalopathy (51%) were at greater risk of intractable epilepsy (P = 0.003), non-ambulance (P = 0.035), ongoing enteral feeds (P < 0.001) and cortical visual impairment (P = 0.007). Serial neuroimaging showed progressive cerebral volume loss in 87.5% and progressive cerebellar atrophy in 70.8%, indicating a neurodegenerative process. Genetic analyses identified 93 unique variants (106 total), including 22 novel variants. Exploratory analyses of genotype-phenotype correlations using unsupervised hierarchical clustering identified novel genotypic predictors of clinical phenotype and long-term outcome with meaningful implications for management. In summary, we expand both the mild and severe phenotypic extremities of the IGDs, provide insights into their neurological basis, and vitally, enable meaningful genetic counselling for affected individuals and their families.

Pediatric nasal chondromesenchymal hamartomas: a case series.

PURPOSE: Nasal chondromesenchymal hamartomas (NCMH) are rare, predominantly benign tumors of the sinonasal tract. The distinction from higher grade malignancy may be challenging based on imaging features alone. To increase the awareness of this entity among radiologists, we present a multi-institutional case series of pediatric NCMH patients showing the varied imaging presentation. METHODS: Descriptive assessment of imaging appearances of the lesions on computed tomography (CT) and magnetic resonance imaging (MRI) was performed. In addition, we reviewed demographic information, clinical data, results of genetic testing, management, and follow-up data. RESULTS: Our case series consisted of 10 patients, with a median age of 0.5 months. Intraorbital and intracranial extensions were both observed in two cases. Common CT findings included bony remodeling, calcifications, and bony erosions. MRI showed heterogeneous expansile lesion with predominantly hyperintense T2 signal and heterogenous post-contrast enhancement in the majority of cases. Most lesions exhibited increased diffusivity on diffusion weighted imaging and showed signal drop-out on susceptibility weighted images in the areas of calcifications. Genetic testing was conducted in 4 patients, revealing the presence of DICER1 pathogenic variant in three cases. Surgery was performed in all cases, with one recurrence in two cases and two recurrences in one case on follow-up. CONCLUSION: NCMHs are predominantly benign tumors of the sinonasal tract, typically associated with DICER1 pathogenic variants and most commonly affecting pediatric population. They may mimic aggressive behavior on imaging; therefore, awareness of this pathology is important. MRI and CT have complementary roles in the diagnosis of this entity.

Imaging panorama of pediatric central nervous system tuberculosis.

Central nervous system tuberculosis (CNS TB) is the most dreaded manifestation of systemic tuberculosis in the pediatric age group. It is associated with high morbidity and mortality due to severe neurological complications and sequelae. Knowledge about the imaging spectrum of CNS TB will help in early presumptive diagnosis and prompt treatment, reducing the development of complications. Imaging also plays a vital role in monitoring the progression of disease after the initiation of antituberculosis therapy. Advanced magnetic resonance imaging (MRI) techniques have recently improved the diagnostic efficacy manifold.In this review, we describe the imaging characteristics, the role of advanced imaging techniques, and follow-up imaging in various types of CNS TB in the pediatric population.

Variation in neuroimaging and outcomes in patients with Sturge Weber syndrome Type III.

OBJECTIVES: Sturge Weber syndrome (SWS) is a neurovascular condition with an estimated incidence of 1 in 20,000 to 50,000 live births. SWS Types I and II involve cutaneous and ophthalmological findings, with neurological involvement in Type I. SWS Type III is exclusive to brain stigmata. Our study aims to describe the characteristics of brain MRI findings and report neuroradiological features with seizure and cognitive outcomes in patients with SWS Type III. METHODS: This is a retrospective case series examining the clinical, radiological, and cognitive characteristics of patients with SWS Type III referred to the SWS Clinic at Boston Children's Hospital. We analyzed brain MRI findings based on vascular and parenchymal features. Clinical and cognitive outcomes were based on a validated assessment tool in this population (Neuroscore). RESULTS: This dedicated case series of patients with Type III SWS from a single center identified ten patients. All patients had classic stigmata indicative of SWS. Two distinct radiological phenotypes were found, one characterized by more pronounced deep venous enlargement, and the other, with more pronounced parenchymal abnormalities. There was heterogeneity in seizure presentation and outcome. Earlier age of onset and seizures predict more severe outcomes, as seen in classic SWS. CONCLUSION: We could not find significant divergence in outcomes between patients with differing neuroimaging phenotypes. These results raise the question of whether the two distinct radiological phenotypes found in SWS Type III are reflective of different disease entities, with underlying genetic heterogeneity. These results suggest the need for larger, multi-center natural history studies.

Somatic Mosaicism in PIK3CA Variant Correlates With Stereoelectroencephalography-Derived Electrophysiology.

Objectives: Brain-limited pathogenic somatic variants are associated with focal pediatric epilepsy, but reliance on resected brain tissue samples has limited our ability to correlate epileptiform activity with abnormal molecular pathology. We aimed to identify the pathogenic variant and map variant allele fractions (VAFs) across an abnormal region of epileptogenic brain in a patient who underwent stereoelectroencephalography (sEEG) and subsequent motor-sparing left frontal disconnection. Methods: We extracted genomic DNA from peripheral blood, brain tissue resected from peri-sEEG electrode regions, and microbulk brain tissue adherent to sEEG electrodes. Samples were mapped based on an anatomic relationship with the presumed seizure onset zone (SOZ). We performed deep panel sequencing of amplified and unamplified DNA to identify pathogenic variants with subsequent orthogonal validation. Results: We detect a pathogenic somatic PIK3CA variant, c.1624G>A (p.E542K), in the brain tissue samples, with VAF inversely correlated with distance from the SOZ. In addition, we identify this variant in amplified electrode-derived samples, albeit with lower VAFs. Discussion: We demonstrate regional mosaicism across epileptogenic tissue, suggesting a correlation between variant burden and SOZ. We also validate a pathogenic variant from individual amplified sEEG electrode-derived brain specimens, although further optimization of techniques is required.

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024.

Analysis of DNA from brain tissue on stereo-EEG electrodes reveals mosaic epilepsy-related variants.

Somatic mosaic variants contribute to focal epilepsy, but genetic analysis has been limited to patients with drug-resistant epilepsy (DRE) who undergo surgical resection, as the variants are mainly brain-limited. Stereoelectroencephalography (sEEG) has become part of the evaluation for many patients with focal DRE, and sEEG electrodes provide a potential source of small amounts of brain-derived DNA. We aimed to identify, validate, and assess the distribution of potentially clinically relevant mosaic variants in DNA extracted from trace brain tissue on individual sEEG electrodes. We enrolled a prospective cohort of eleven pediatric patients with DRE who had sEEG electrodes implanted for invasive monitoring, one of whom was previously reported. We extracted unamplified DNA from the trace brain tissue on each sEEG electrode and also performed whole-genome amplification for each sample. We extracted DNA from resected brain tissue and blood/saliva samples where available. We performed deep panel and exome sequencing on a subset of samples from each case and analysis for potentially clinically relevant candidate germline and mosaic variants. We validated candidate mosaic variants using amplicon sequencing and assessed the variant allele fraction (VAF) in amplified and unamplified electrode-derived DNA and across electrodes. We extracted DNA from >150 individual electrodes from 11 individuals and obtained higher concentrations of whole-genome amplified vs unamplified DNA. Immunohistochemistry confirmed the presence of neurons in the brain tissue on electrodes. Deep sequencing and analysis demonstrated similar depth of coverage between amplified and unamplified samples but significantly more called mosaic variants in amplified samples. In addition to the mosaic PIK3CA variant detected in a previously reported case from our group, we identified and validated four potentially clinically relevant mosaic variants in electrode-derived DNA in three patients who underwent laser ablation and did not have resected brain tissue samples available. The variants were detected in both amplified and unamplified electrode-derived DNA, with higher VAFs observed in DNA from electrodes in closest proximity to the electrical seizure focus in some cases. This study demonstrates that mosaic variants can be identified and validated from DNA extracted from trace brain tissue on individual sEEG electrodes in patients with drug-resistant focal epilepsy and in both amplified and unamplified electrode-derived DNA samples. Our findings support a relationship between the extent of regional genetic abnormality and electrophysiology, and suggest that with further optimization, this minimally invasive diagnostic approach holds promise for advancing precision medicine for patients with DRE as part of the surgical evaluation.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.