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.

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.

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. METHODS: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. RESULTS: Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). CONCLUSIONS: DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.

Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

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.

3D printed temporal bones for preoperative simulation and planning.

OBJECTIVE: Demonstrate the utility of 3D printed temporal bone models in individual patient preoperative planning and simulation. METHODS: 3D models of the temporal bone were made from 5 pediatric and adult patients at a tertiary academic hospital with challenging surgical anatomy planned for cochlear implantation or exteriorization of cholesteatoma with complex labyrinthine fistula. The 3D models were created from CT scan used for preoperative planning, simulation and intraoperative reference. The utility of models was assessed for ease of segmentation and production and impact on surgery in regard to reducing intraoperative time and costs, improving safety and efficacy. RESULTS: Three patients received cochlear implants, two exteriorization of advanced cholesteatoma with fistulas (1 internal auditory canal/cochlea, 1 all three semicircular canals). Surgical planning and intraoperative referencing to the simulations by the attending surgeon and trainees significantly altered original surgical plans. In a case of X-linked hereditary deafness, optimal angles and rotation maneuvers for cochlear implant insertion reduced operating time by 93 min compared to the previous contralateral side surgery. Two cochlear implant cases planned for subtotal petrosectomy approach due to aberrant anatomy were successfully approached through routine mastoidectomy. The cholesteatoma cases were successfully exteriorized without necessitating partial labyrinthectomy or labyrinthine injury. There were no complications. CONCLUSION: 3D printed models for simulation training, surgical planning and use intraoperatively in temporal bone surgery demonstrated significant benefits in designing approaches, development of patient-specific techniques, avoidance of potential or actual complications encountered in previous or current surgery, and reduced surgical time and costs.

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.

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.

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.

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.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

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.

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.

Purpose: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. Materials and Methods: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wildtype) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer and self-supervised approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist, that quantifies the accuracy of 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 macro-average AUC (0.82 [95% CI: 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%). Conclusion: Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning.

Purpose: Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation. Methods: We leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach. The best model [via Dice similarity coefficient (DSC)] was externally validated and subject to randomized, blinded evaluation by three expert clinicians wherein clinicians assessed clinical acceptability of expert- and AI-generated segmentations via 10-point Likert scales and Turing tests. Results: The best AI model utilized in-domain, stepwise transfer learning (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline model (median DSC 0.812 [IQR 0.559-0.888]; p<0.05). On external testing (n=60), the AI model yielded accuracy comparable to inter-expert agreement (median DSC: 0.834 [IQR 0.726-0.901] vs. 0.861 [IQR 0.795-0.905], p=0.13). On clinical benchmarking (n=100 scans, 300 segmentations from 3 experts), the experts rated the AI model higher on average compared to other experts (median Likert rating: 9 [IQR 7-9]) vs. 7 [IQR 7-9], p<0.05 for each). Additionally, the AI segmentations had significantly higher (p<0.05) overall acceptability compared to experts on average (80.2% vs. 65.4%). Experts correctly predicted the origins of AI segmentations in an average of 26.0% of cases. Conclusions: Stepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. This approach may enable development and translation of AI imaging segmentation algorithms in limited data scenarios.

Orbital and Eyelid Characteristics, Strabismus, and Intracranial Pressure Control in Apert Children Treated by Endoscopic Strip Craniectomy versus Fronto-Orbital Advancement.

Apert syndrome is characterized by eyelid dysmorphology, V-pattern strabismus, extraocular muscle excyclorotation, and elevated intracranial pressure (ICP). We compare eyelid characteristics, severity of V-pattern strabismus, rectus muscle excyclorotation, and ICP control in Apert syndrome patients initially treated by endoscopic strip craniectomy (ESC) at about 4 months of age versus fronto-orbital advancement (FOA) performed about 1 year of age. Methods: Twenty-five patients treated at Boston Children's Hospital met inclusion criteria for this retrospective cohort study. Primary outcomes were magnitude of palpebral fissure downslanting at 1, 3, and 5 years of age, severity of V-pattern strabismus, rectus muscle excyclorotation, and interventions to control ICP. Results: Before craniofacial repair and through 1 year of age, none of the studied parameters differed for FOA versus ESC treated patients. Palpebral fissure downslanting became statistically greater for those treated by FOA by 3 (P < 0.001) and 5 years of age (P = 0.001). Likewise, severity of palpebral fissure downslanting correlated with severity of V-pattern strabismus at 3 (P = 0.004) and 5 (P = 0.002) years of age. Palpebral fissure downslanting and rectus muscle excyclorotation were typically coexistent (P = 0.053). Secondary interventions to control ICP were required in four of 14 patients treated by ESC (primarily FOA) and in two of 11 patients initially treated by FOA (primarily third ventriculostomy) (P = 0.661). Conclusions: Apert patients initially treated by ESC had less severe palpebral fissure downslanting and V-pattern strabismus, normalizing their appearance. Thirty percent initially treated by ESC required secondary FOA to control ICP.

Viewpoints from the National Consultation on Addressing Acute Malnutrition on Mainstreaming Community-Based Program for Management of Acute Malnutrition in India.

High burden of acute malnutrition among children less than 5 years is a major public health problem in India. A "Two-days National Consultation on Addressing Acute Malnutrition" was organized to gather experiences and evidence from 13 states of India on prevention and management of acute malnutrition among children and documenting viewpoints from experts and government counterparts on the same. The consultation centered around five key themes of addressing acute malnutrition; 1) capacity building, 2) strengthening screening, 3) nutritional care of wasting, 4) tracking progress, and 5) scale-up. The paper highlights the experiences and key recommendations around the above key themes. It emerged that there is a need to further accelerate the efforts toward strengthening existing platforms and services to address acute malnutrition among children. Regular trainings of the frontline workers, increased convergence, regular monitoring, and continued service delivery during the pandemic should be undertaken for better outcomes.

Stereoelectroencephalography followed by combined electrode removal and MRI-guided laser interstitial thermal therapy or open resection: a single-center series in pediatric patients with medically refractory epilepsy.

OBJECTIVE: Stereoelectroencephalography (SEEG) and MRI-guided laser interstitial thermal therapy (MRgLITT) have emerged as safe, effective, and less invasive alternatives to subdural grid placement and open resection, respectively, for the localization and treatment of medically refractory epilepsy (MRE) in children. Reported pediatric experience combining these complementary techniques is limited, with traditional workflows separating electrode removal and ablation/resection. The authors describe the largest reported series of pediatric epilepsy patients who underwent MRgLITT following SEEG contrasted with a cohort that underwent craniotomy following SEEG, combining ablation/resection with electrode explantation as standard practice. METHODS: The medical records of all patients with MRE who had undergone SEEG followed by MRgLITT or open resection/disconnection at Boston Children's Hospital between November 2015 and December 2020 were retrospectively reviewed. Primary outcome variables included surgical complication rates, length of hospital stay following treatment, and Engel classification at the last follow-up. RESULTS: Of 74 SEEG patients, 27 (median age 12.1 years, 63% female) underwent MRgLITT and 47 (median age 12.1 years, 49% female) underwent craniotomy. Seventy patients (95%) underwent SEEG followed by combined electrode removal and treatment. Eight MRgLITT cases (30%) and no open cases targeted the insula (p < 0.001). Complication rates did not differ, although trends toward more subdural/epidural hematomas, infarcts, and permanent unanticipated neurological deficits were evident following craniotomy, whereas a trend toward more temporary unanticipated neurological deficits was seen following MRgLITT. The median duration of hospitalization after treatment was 3 and 5 days for MRgLITT and open cases, respectively (p = 0.078). Seizure outcomes were similar between the cohorts, with 74% of MRgLITT and craniotomy patients attaining Engel class I or II outcomes (p = 0.386) at the last follow-up (median 1.1 and 1.9 years, respectively). CONCLUSIONS: MRgLITT and open resection following SEEG can both effectively treat MRE in pediatric patients and generally can be performed in a two-surgery workflow during a single hospitalization. In appropriately selected patients, MRgLITT tended to be associated with shorter hospitalizations and fewer complications following treatment and may be best suited for focal deep-seated targets associated with relatively challenging open surgical approaches.

Mimics of Pediatric Small Vessel Primary Angiitis of the Central Nervous System.

OBJECTIVE: Small vessel primary angiitis of the central nervous system is a rare and often severe disease characterized by central nervous system-restricted inflammatory vasculitis on histopathology. Diagnosis requires brain biopsy for confirmation and is suggested prior to starting immunotherapy when feasible. However, emerging evidence suggests that other neuroinflammatory conditions may have a clinical and radiographic phenotype that mimics small vessel primary angiitis, at times with overlapping pathologic features as well. Such diagnoses, including myelin oligodendrocyte glycoprotein antibody-associated disease and central nervous system-restricted hemophagocytic lymphohistiocytosis, can be non-invasively diagnosed with serum antibody or genetic testing that would prompt different monitoring and treatment paradigms. To determine the ultimate diagnosis of patients who were suspected prior to biopsy to have small vessel primary angiitis, we reviewed the clinical, radiographic, and pathological features of a cohort of patients at a single center undergoing brain biopsy for non-oncologic indications. METHODS: Clinical data were retrospectively extracted from the medical record. Pathology and neuroimaging review was conducted. RESULTS: We identified 21 patients over a 19-year time-period, of whom 14 (66.7%) were ultimately diagnosed with entities other than small vessel primary angiitis that would have obviated the need for brain biopsy. Diagnoses included anti-myelin oligodendrocyte glycoprotein antibody associated disease (n = 9), central nervous system-restricted hemophagocytic lymphohistiocytosis (n = 3), anti-GABAA receptor encephalitis (n = 1), and Aicardi-Goutières syndrome (n = 1). INTERPRETATION: This study highlights the importance of pursuing now readily available non-invasive testing for mimicking diagnoses before performing a brain biopsy for suspected small vessel primary angiitis of the central nervous system. ANN NEUROL 2023;93:109-119.

Limited utility of structural MRI to identify the epileptogenic zone in young children with tuberous sclerosis.

BACKGROUND AND PURPOSE: The success of epilepsy surgery in children with tuberous sclerosis complex (TSC) hinges on identification of the epileptogenic zone (EZ). We studied structural MRI markers of epileptogenic lesions in young children with TSC. METHODS: We included 26 children with TSC who underwent epilepsy surgery before the age of 3 years at five sites, with 12 months or more follow-up. Two neuroradiologists, blinded to surgical outcome data, reviewed 10 candidate lesions on preoperative MRI for characteristics of the tuber (large affected area, calcification, cyst-like properties) and of focal cortical dysplasia (FCD) features (cortical malformation, gray-white matter junction blurring, transmantle sign). They selected lesions suspect for the EZ based on structural MRI, and reselected after unblinding to seizure onset location on electroencephalography (EEG). RESULTS: None of the tuber characteristics and FCD features were distinctive for the EZ, indicated by resected lesions in seizure-free children. With structural MRI alone, the EZ was identified out of 10 lesions in 31%, and with addition of EEG data, this increased to 48%. However, rates of identification of resected lesions in non-seizure-free children were similar. Across 251 lesions, interrater agreement was moderate for large size (κ = .60), and fair (κ = .24) for all other features. CONCLUSIONS: In young children with TSC, the utility of structural MRI features is limited in the identification of the epileptogenic tuber, but improves when combined with EEG data.