Imaging of pediatric spine and spinal cord tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper.

Childhood spinal tumors are rare. Tumors can involve the spinal cord, the meninges, bony spine, and the paraspinal tissue. Optimized imaging should be utilized to evaluate tumors arising from specific spinal compartments. This paper provides consensus-based recommendations for optimized imaging of tumors arising from specific spinal compartments at diagnosis, follow-up during and after therapy, and response assessment.

Response assessment in paediatric high-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group.

Response criteria for paediatric high-grade glioma vary historically and across different cooperative groups. The Response Assessment in Neuro-Oncology working group developed response criteria for adult high-grade glioma, but these were not created to meet the unique challenges in children with the disease. The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric high-grade glioma. Current practice and literature were reviewed to identify major challenges in assessing the response of paediatric high-grade gliomas to various treatments. For areas in which scientific investigation was scarce, consensus was reached through an iterative process. RAPNO response assessment recommendations include the use of MRI of the brain and the spine, assessment of clinical status, and the use of corticosteroids or antiangiogenics. Imaging standards for brain and spine are defined. Compared with the recommendations for the management of adult high-grade glioma, for paediatrics there is inclusion of diffusion-weighted imaging and a higher reliance on T2-weighted fluid-attenuated inversion recovery. Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.

Pediatric brain tumors.

Among all causes of death in children from solid tumors, pediatric brain tumors are the most common. This article includes an overview of a subset of infratentorial and supratentorial tumors with a focus on tumor imaging features and molecular advances and treatments of these tumors. Key to understanding the imaging features of brain tumors is a firm grasp of other disease processes that can mimic tumor on imaging. We also review imaging features of a common subset of tumor mimics.

Cortically-based Brain Tumors in Children: A Decision-tree Approach in the Radiology Reading Room.

Cortically-based brain tumors in children constitute a unique set of tumors with variably aggressive biological behavior. As radiologists play an integral role on the multidisciplinary medical team, a clinically useful and easy-to-follow flowchart for the differential diagnoses of these complex brain tumors is essential.This proposed algorithm tree provides the latest insights into the typical imaging characteristics and epidemiologic data that differentiate the tumor entities, taking into perspective the 2021 World Health Organization's classification and highlighting classic as well as newly identified pathologic subtypes using current molecular understanding.ABBREVIATIONS: Astroblastoma=AB) Angiocentric glioma (AG) Atypical teratoid rhabdoid tumor (ATRT) Central Nervous System tumor (CNS) CNS neuroblastoma FOXR2-activated (NB-FOXR2) Desmoplastic infantile glioma/astrocytoma (DIG/DIA) Diffuse hemispheric glioma, H3 G34-mutant (DHG) Diffuse glioneuronal tumor with oligodendroglioma-like features and nuclear clusters (DGONC) Dysembryoplastic neuroepithelial tumor (DNET) Embryonal Tumors with Multilayered Rosettes (ETMR) Ependymoma (EP) Focal cortical dysplasia (FCD) Ganglioglioma/gangliocytoma (GG) Infant-type hemispheric glioma (IHG) Intracranial pressure (ICP) Long-term epilepsy-associated tumors (LEATs) Pediatric diffuse low-grade gliomas (pLGG) MR spectroscopy (MRS) Multinodular and vacuolating neuronal tumor (MVNT) Overall survival (OS) Pediatric diffuse high-grade gliomas (pHGG).

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.

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.

Challenges with defining response to antitumor agents in pediatric neuro-oncology: a report from the response assessment in pediatric neuro-oncology (RAPNO) working group.

Criteria for new drug approval include demonstration of efficacy. In neuro-oncology, this is determined radiographically utilizing tumor measurements on MRI scans. Limitations of this method have been identified where drug activity is not reflected in decreased tumor size. The RANO (Response Assessment in Neuro-Oncology) working group was established to address limitations in defining endpoints for clinical trials in adult neuro-oncology and to develop standardized response criteria. RAPNO was subsequently established to address unique issues in pediatric neuro-oncology. The aim of this paper is to delineate response criteria issues in pediatric clinical trials as a basis for subsequent recommendations.

Summary of the 2022 ACR Intersociety Meeting.

The ACR Intersociety Committee meeting of 2022 (ISC-2022) was convened around the theme of "Recovering From The Great Resignation, Moral Injury and Other Stressors: Rebuilding Radiology for a Robust Future." Representatives from 29 radiology organizations, including all radiology subspecialties, radiation oncology, and medical physics, as well as academic and private practice radiologists, met for 3 days in early August in Park City, Utah, to search for solutions to the most pressing problems facing the specialty of radiology in 2022. Of these, the mismatch between the clinical workload and the available radiologist workforce was foremost-as many other identifiable problems flowed downstream from this, including high job turnover, lack of time for teaching and research, radiologist burnout, and moral injury.

Automated temporalis muscle quantification and growth charts for children through adulthood.

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Neurological and Psychological Sequelae Associated With Multisystem Inflammatory Syndrome in Children.

Importance: Acute neurological involvement occurs in some patients with multisystem inflammatory syndrome in children (MIS-C), but few data report neurological and psychological sequelae, and no investigations include direct assessments of cognitive function 6 to 12 months after discharge. Objective: To characterize neurological, psychological, and quality of life sequelae after MIS-C. Design, Setting, and Participants: This cross-sectional cohort study was conducted in the US and Canada. Participants included children with MIS-C diagnosed from November 2020 through November 2021, 6 to 12 months after hospital discharge, and their sibling or community controls, when available. Data analysis was performed from August 2022 to May 2023. Exposure: Diagnosis of MIS-C. Main Outcomes and Measures: A central study site remotely administered a onetime neurological examination and in-depth neuropsychological assessment including measures of cognition, behavior, quality of life, and daily function. Generalized estimating equations, accounting for matching, assessed for group differences. Results: Sixty-four patients with MIS-C (mean [SD] age, 11.5 [3.9] years; 20 girls [31%]) and 44 control participants (mean [SD] age, 12.6 [3.7] years; 20 girls [45%]) were enrolled. The MIS-C group exhibited abnormalities on neurological examination more frequently than controls (15 of 61 children [25%] vs 3 of 43 children [7%]; odds ratio, 4.7; 95% CI, 1.3-16.7). Although the 2 groups performed similarly on most cognitive measures, the MIS-C group scored lower on the National Institutes of Health Cognition Toolbox List Sort Working Memory Test, a measure of executive functioning (mean [SD] scores, 96.1 [14.3] vs 103.1 [10.5]). Parents reported worse psychological outcomes in cases compared with controls, particularly higher scores for depression symptoms (mean [SD] scores, 52.6 [13.1] vs 47.8 [9.4]) and somatization (mean [SD] scores, 55.5 [15.5] vs 47.0 [7.6]). Self-reported (mean [SD] scores, 79.6 [13.1] vs 85.5 [12.3]) and parent-reported (mean [SD] scores, 80.3 [15.5] vs 88.6 [13.0]) quality of life scores were also lower in cases than controls. Conclusions and Relevance: In this cohort study, compared with contemporaneous sibling or community controls, patients with MIS-C had more abnormal neurologic examinations, worse working memory scores, more somatization and depression symptoms, and lower quality of life 6 to 12 months after hospital discharge. Although these findings need to be confirmed in larger studies, enhanced monitoring may be warranted for early identification and treatment of neurological and psychological symptoms.

Changes in Distribution of Severe Neurologic Involvement in US Pediatric Inpatients With COVID-19 or Multisystem Inflammatory Syndrome in Children in 2021 vs 2020.

Importance: In 2020 during the COVID-19 pandemic, neurologic involvement was common in children and adolescents hospitalized in the United States for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related complications. Objective: To provide an update on the spectrum of SARS-CoV-2-related neurologic involvement among children and adolescents in 2021. Design, Setting, and Participants: Case series investigation of patients reported to public health surveillance hospitalized with SARS-CoV-2-related illness between December 15, 2020, and December 31, 2021, in 55 US hospitals in 31 states with follow-up at hospital discharge. A total of 2253 patients were enrolled during the investigation period. Patients suspected of having multisystem inflammatory syndrome in children (MIS-C) who did not meet criteria (n = 85) were excluded. Patients (<21 years) with positive SARS-CoV-2 test results (reverse transcriptase-polymerase chain reaction and/or antibody) meeting criteria for MIS-C or acute COVID-19 were included in the analysis. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening neurologic involvement was adjudicated by experts based on clinical and/or neuroradiological features. Type and severity of neurologic involvement, laboratory and imaging data, vaccination status, and hospital discharge outcomes (death or survival with new neurologic deficits). Results: Of 2168 patients included (58% male; median age, 10.3 years), 1435 (66%) met criteria for MIS-C, and 476 (22%) had documented neurologic involvement. Patients with neurologic involvement vs without were older (median age, 12 vs 10 years) and more frequently had underlying neurologic disorders (107 of 476 [22%] vs 240 of 1692 [14%]). Among those with neurologic involvement, 42 (9%) developed acute SARS-CoV-2-related life-threatening conditions, including central nervous system infection/demyelination (n = 23; 15 with possible/confirmed encephalitis, 6 meningitis, 1 transverse myelitis, 1 nonhemorrhagic leukoencephalopathy), stroke (n = 11), severe encephalopathy (n = 5), acute fulminant cerebral edema (n = 2), and Guillain-Barré syndrome (n = 1). Ten of 42 (24%) survived with new neurologic deficits at discharge and 8 (19%) died. Among patients with life-threatening neurologic conditions, 15 of 16 vaccine-eligible patients (94%) were unvaccinated. Conclusions and Relevance: SARS-CoV-2-related neurologic involvement persisted in US children and adolescents hospitalized for COVID-19 or MIS-C in 2021 and was again mostly transient. Central nervous system infection/demyelination accounted for a higher proportion of life-threatening conditions, and most vaccine-eligible patients were unvaccinated. COVID-19 vaccination may prevent some SARS-CoV-2-related neurologic complications and merits further study.

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors. METHODS: The preoperative and postoperative cohorts were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO (Response Assessment in Pediatric Neuro-Oncology) criteria (AutoRAPNO) was determined. Performance was compared to that of 2 expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared. RESULTS: A total of 794 preoperative MRIs from 794 patients and 1003 postoperative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with intraclass correlation coefficients (ICCs) of 0.912 and 0.960 for the 2 preoperative and 0.947 and 0.896 for the 2 postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC = 0.909; Rater 3 ICC = 0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions. CONCLUSIONS: Our automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.

Outcome of fetuses with cerebral ventriculomegaly and septum pellucidum leaflet abnormalities.

OBJECTIVE: The objective of this study was to assess the outcomes of the prenatal diagnosis of septal leaflet abnormalities in fetuses referred for prenatal imaging with a finding of ventriculomegaly. MATERIALS AND METHODS: This study is a retrospective review of fetuses with a diagnostic code of septal leaflet abnormalities from a larger prospective study. Four hundred twenty-five pregnant women with 433 fetuses referred for ventriculomegaly were imaged with ultrasound and MRI between July 1, 2003, and May 15, 2009. Four to six radiologists independently reviewed sonographic and MR images and recorded lateral ventricular diameters at the atrium and frontal horns, ventricular configuration, and the presence of ventriculomegaly and of other CNS abnormalities. Final prenatal ultrasound, MRI, and overall diagnoses were decided by consensus. Fetuses with a diagnostic code of septal leaflet abnormalities were identified, and birth outcome, autopsy findings, postnatal imaging, and postnatal follow-up examinations were obtained. The analysis of covariance, controlling for gestational age, was used to compare ventricular dimensions between fetuses with septal leaflet abnormalities and fetuses with isolated ventriculomegaly. Interrater agreement for the detection of septal leaflet abnormalities was assessed with kappa statistics. Interrater agreement and intrarater agreement for frontal horn measurements were assessed by variance components analysis. RESULTS: Twenty-three fetuses had septal leaflet abnormalities and 229 had isolated ventriculomegaly. Atrial and frontal horn diameters, adjusted for gestational age, were 77% and 98% larger, respectively, in fetuses with septal leaflet abnormalities than in fetuses with isolated ventriculomegaly (p < 0.0001). Before the consensus conference, agreement among ultrasound readers was moderate (κ = 0.54) and among MR readers, good (κ = 0.69). Additional CNS findings were seen on MRI in 12 of 23 fetuses (52%). Eleven pregnancies with septal leaflet abnormalities underwent termination and 12 progressed to livebirth; of the 12 livebirths, three neonates died. Neurodevelopmental follow-up was abnormal in all surviving children. CONCLUSION: A septal leaflet abnormality in the setting of ventriculomegaly is most frequently associated with other CNS abnormalities and is associated with postnatal developmental delay.

Ultrasound and MRI of fetuses with ventriculomegaly: can cortical development be used to predict postnatal outcome?

OBJECTIVE: The purpose of this study was to assess the conspicuity of brain cortical maturation with sonography and MRI of fetuses referred because of ventriculomegaly and to determine whether sulcal visualization can be used to predict postnatal outcome. SUBJECTS AND METHODS: Women with 374 fetuses referred because of ventriculomegaly underwent sonography and MRI. Four to six radiologists rated visualization of 19 fissures or sulci. Majority opinion regarding sulcal visualization was compared among fetuses categorized by CNS abnormality: normal, isolated ventriculomegaly, and ventriculomegaly with additional CNS abnormalities. Live-born infants were categorized as having normal or abnormal development. Logistic regression analysis was used to correlate sulcal visualization and postnatal development. A subanalysis was performed with fetuses who had been classified prenatally as having a normal brain or isolated ventriculomegaly. RESULTS: Cortical sulci were visualized more frequently and at an earlier gestational age with MRI than with ultrasound. In the entire cohort, the odds ratio of normal development ranged from 3.1 to 10.0 whenever the calcarine, parietooccipital, cingulate, superior temporal, precentral, or postcentral sulcus was seen on MR images. In fetuses categorized as having a normal brain or having isolated ventriculomegaly, the odds ratio of normal development ranged from 3.5 to 9.0 whenever the parietooccipital, cingulate, or superior temporal sulcus was seen. CONCLUSION: Visualization of the sulci in fetal brains depends on the imaging modality used and the gestational age at imaging. Information regarding sulcal visualization may aid in counseling patients carrying fetuses with ventriculomegaly.

Magnetic resonance volumetric assessments of brains in fetuses with ventriculomegaly correlated to outcomes.

OBJECTIVES: The purpose of this study was to correlate 2-dimensional magnetic resonance (MR) measurements of lateral ventricular width and 3-dimensional measurements of lateral ventricular and supratentorial parenchymal volumes to postnatal outcomes in fetuses with ventriculomegaly. METHODS: A total of 307 fetuses (mean gestational age, 26.0 weeks; range, 15.7-39.4 weeks) had MR volumetry after referral for ventriculomegaly. Fetuses were grouped into those with (n = 114) and without (n = 193) other central nervous system (CNS) anomalies. Pregnancy and postnatal neurodevelopmental outcomes up to 3 years of age were obtained. A subgroup analysis was performed excluding fetuses with other CNS anomalies. Logistic regression analysis was performed to assess which measurement was most predictive of outcomes. RESULTS: There were 50 terminations, 2 stillbirths, and 255 live births. Seventy-five cases were lost to follow-up. Among 180 live-born neonates with follow-up, 140 had abnormal and 40 had normal outcomes. Atrial diameter (P < .0001), frontal horn diameter (P < .0001), and ventricular volume (P = .04) were predictive of live birth, with 92% specificity at 60% sensitivity. Among fetuses without other CNS anomalies, 180 of 193 pregnancies (93%) resulted in live deliveries, with atrial diameter (P < .0001), frontal horn diameter (P = .003), and ventricular volume (P = .008) associated with live birth and atrial diameter having the highest specificity (>99% at 60% sensitivity). Parenchymal volume was not associated with normal or abnormal outcomes (either live birth versus death or normal versus abnormal neurodevelopmental outcome). Among live-born neonates, no age-adjusted threshold for any of the measurements reliably distinguished between normal and abnormal neurodevelopmental outcomes. CONCLUSIONS: Ventricular volume and diameter, but not parenchymal volume, correlate with live birth in fetuses with ventriculomegaly. However, once live born, neither 2- nor 3-dimensional measurements can distinguish a fetus that will have a normal outcome.

Multisystem Inflammatory-like Syndrome in a Child Following COVID-19 mRNA Vaccination.

A 12-year-old male was presented to the hospital with acute encephalopathy, headache, vomiting, diarrhea, and elevated troponin after recent COVID-19 vaccination. Two days prior to admission and before symptom onset, he received the second dose of the Pfizer-BioNTech COVID-19 vaccine. Symptoms developed within 24 h with worsening neurologic symptoms, necessitating admission to the pediatric intensive care unit. Brain magnetic resonance imaging within 16 h of admission revealed a cytotoxic splenial lesion of the corpus callosum (CLOCC). Nineteen days prior to admission, he developed erythema migrans, and completed an amoxicillin treatment course for clinical Lyme disease. However, Lyme antibody titers were negative on admission and nine days later, making active Lyme disease an unlikely explanation for his presentation to hospital. An extensive workup for other etiologies on cerebrospinal fluid and blood samples was negative, including infectious and autoimmune causes and known immune deficiencies. Three weeks after hospital discharge, all of his symptoms had dissipated, and he had a normal neurologic exam. Our report highlights a potential role of mRNA vaccine-induced immunity leading to MIS-C-like symptoms with cardiac involvement and a CLOCC in a recently vaccinated child and the complexity of establishing a causal association with vaccination. The child recovered without receipt of immune modulatory treatment.

Neurologic Involvement in Children and Adolescents Hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome.

Importance: Coronavirus disease 2019 (COVID-19) affects the nervous system in adult patients. The spectrum of neurologic involvement in children and adolescents is unclear. Objective: To understand the range and severity of neurologic involvement among children and adolescents associated with COVID-19. Setting, Design, and Participants: Case series of patients (age <21 years) hospitalized between March 15, 2020, and December 15, 2020, with positive severe acute respiratory syndrome coronavirus 2 test result (reverse transcriptase-polymerase chain reaction and/or antibody) at 61 US hospitals in the Overcoming COVID-19 public health registry, including 616 (36%) meeting criteria for multisystem inflammatory syndrome in children. Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening involvement was adjudicated by experts based on clinical and/or neuroradiologic features. Exposures: Severe acute respiratory syndrome coronavirus 2. Main Outcomes and Measures: Type and severity of neurologic involvement, laboratory and imaging data, and outcomes (death or survival with new neurologic deficits) at hospital discharge. Results: Of 1695 patients (909 [54%] male; median [interquartile range] age, 9.1 [2.4-15.3] years), 365 (22%) from 52 sites had documented neurologic involvement. Patients with neurologic involvement were more likely to have underlying neurologic disorders (81 of 365 [22%]) compared with those without (113 of 1330 [8%]), but a similar number were previously healthy (195 [53%] vs 723 [54%]) and met criteria for multisystem inflammatory syndrome in children (126 [35%] vs 490 [37%]). Among those with neurologic involvement, 322 (88%) had transient symptoms and survived, and 43 (12%) developed life-threatening conditions clinically adjudicated to be associated with COVID-19, including severe encephalopathy (n = 15; 5 with splenial lesions), stroke (n = 12), central nervous system infection/demyelination (n = 8), Guillain-Barré syndrome/variants (n = 4), and acute fulminant cerebral edema (n = 4). Compared with those without life-threatening conditions (n = 322), those with life-threatening neurologic conditions had higher neutrophil-to-lymphocyte ratios (median, 12.2 vs 4.4) and higher reported frequency of D-dimer greater than 3 µg/mL fibrinogen equivalent units (21 [49%] vs 72 [22%]). Of 43 patients who developed COVID-19-related life-threatening neurologic involvement, 17 survivors (40%) had new neurologic deficits at hospital discharge, and 11 patients (26%) died. Conclusions and Relevance: In this study, many children and adolescents hospitalized for COVID-19 or multisystem inflammatory syndrome in children had neurologic involvement, mostly transient symptoms. A range of life-threatening and fatal neurologic conditions associated with COVID-19 infrequently occurred. Effects on long-term neurodevelopmental outcomes are unknown.

Phase II study of peginterferon alpha-2b for patients with unresectable or recurrent craniopharyngiomas: a Pediatric Brain Tumor Consortium report.

BACKGROUND: Craniopharyngiomas account for approximately 1.2-4% of all CNS tumors. They are typically treated with a combination of surgical resection and focal radiotherapy. Unfortunately, treatment can lead to permanent deleterious effects on behavior, learning, and endocrine function. METHODS: The Pediatric Brain Tumor Consortium performed a multicenter phase 2 study in children and young adults with unresectable or recurrent craniopharyngioma (PBTC-039). Between December 2013 and November 2017, nineteen patients (median age at enrollment, 13.1 y; range, 2-25 y) were enrolled in one of 2 strata: patients previously treated with surgery alone (stratum 1) or who received radiation (stratum 2). RESULTS: Eighteen eligible patients (8 male, 10 female) were treated with weekly subcutaneous pegylated interferon alpha-2b for up to 18 courses (108 wk). Therapy was well tolerated with no grade 4 or 5 toxicities. 2 of the 7 eligible patients (28.6%) in stratum 1 had a partial response, but only one response was sustained for more than 3 months. None of the 11 stratum 2 patients had an objective radiographic response, although median progression-free survival was 19.5 months. CONCLUSIONS: Pegylated interferon alpha-2b treatment, in lieu of or following radiotherapy, was well tolerated in children and young adults with recurrent craniopharyngiomas. Although objective responses were limited, progression-free survival results are encouraging, warranting further studies.

Brainstem Injury in Pediatric Patients Receiving Posterior Fossa Photon Radiation.

PURPOSE: Brainstem necrosis is a rare, but dreaded complication of radiation therapy; however, data on the incidence of brainstem injury for tumors involving the posterior fossa in photon-treated patient cohorts are still needed. METHODS AND MATERIALS: Clinical characteristics and dosimetric parameters were recorded for 107 pediatric patients who received photon radiation for posterior fossa tumors without brainstem involvement from 2000 to 2016. Patients were excluded if they received a prescription dose <50.4 Gy, a brainstem maximum dose <50.4 Gy, or had fewer than 2 magnetic resonance imaging scans within 18 months after radiation. Post-radiation therapy magnetic resonance imaging findings were recorded, and brainstem toxicity was graded using National Cancer Institute Common Terminology Criteria for Adverse Events, version 5. RESULTS: The most common histologies were medulloblastoma (61.7%) and ependymoma (15.9%), and median age at diagnosis was 8.3 years (range, 0.8-20.7). Sixty-seven patients (62.6%) received craniospinal irradiation (median, 23.4 Gy; range, 18.0-39.6) as a component of their radiation therapy, and 39.3% and 40.2% of patients received an additional involved field or whole posterior fossa boost, respectively. Median prescribed dose was 55.8 Gy (range, 50.4-60.0). Median clinical and imaging follow-up were 4.7 years (range, 0.1-17.5) and 4.2 years (range, 0.1-17.3), respectively. No grade ≥2 toxicities were observed. The incidence of grade 1 brainstem necrosis was 1.9% (2 of 107). These patients were by definition asymptomatic and experienced resolution of imaging abnormality after 5.3 months and 2.1 years, respectively. CONCLUSIONS: Risk of brainstem necrosis was minimal in this multi-institutional study of pediatric patients treated with photon radiation therapy for tumors involving the posterior fossa with no cases of symptomatic brainstem injury, suggesting that brainstem injury risk is minimal in patients treated with photon therapy.