Faculty Bibliography

Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Here, we report brain imaging features in 11 critically ill COVID-19 patients with persistently depressed mental status who underwent MRI between April 5-25, 2020 at our institution. These features include, 1) Confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients), and 2) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in 7 of 11 patients). We also discuss potential pathogeneses.

The SET domain containing 2, histone lysine methyltransferase encoded by SETD2 is a dual-function methyltransferase for histones and microtubules and plays an important role for transcriptional regulation, genomic stability, and cytoskeletal functions. Specifically, SETD2 is associated with trimethylation of histone H3 at lysine 36 (H3K36me3) and methylation of α-tubulin at lysine 40. Heterozygous loss of function and missense variants have previously been described with Luscan-Lumish syndrome (LLS), which is characterized by overgrowth, neurodevelopmental features, and absence of overt congenital anomalies. We have identified 15 individuals with de novo variants in codon 1740 of SETD2 whose features differ from those with LLS. Group 1 consists of 12 individuals with heterozygous variant c.5218C>T p.(Arg1740Trp) and Group 2 consists of 3 individuals with heterozygous variant c.5219G>A p.(Arg1740Gln). The phenotype of Group 1 includes microcephaly, profound intellectual disability, congenital anomalies affecting several organ systems, and similar facial features. Individuals in Group 2 had moderate to severe intellectual disability, low normal head circumference, and absence of additional major congenital anomalies. While LLS is likely due to loss of function of SETD2, the clinical features seen in individuals with variants affecting codon 1740 are more severe suggesting an alternative mechanism, such as gain of function, effects on epigenetic regulation, or posttranslational modification of the cytoskeleton. Our report is a prime example of different mutations in the same gene causing diverging phenotypes and the features observed in Group 1 suggest a new clinically recognizable syndrome uniquely associated with the heterozygous variant c.5218C>T p.(Arg1740Trp) in SETD2.

Background: The relationship between anatomical variations in circle of Willis and brain infarction is controversial. The purpose of this study was to evaluate the relationship between anatomical variations in posterior portion of the circle of Willis assessed by MR angiography (MRA) and ischemic infarction in different brain territories. Methods: This cross-sectional study was conducted on consecutive patients who underwent brain MRI and MRA for suspected cerebrovascular accident. The frequency of anatomical variations including persistent fetal origin of posterior cerebral artery (fPCA) and hypoplastic/aplastic posterior communicating artery (PCoA) and their association with infarction in different intracranial vascular territories was assessed. Results: In total, 298 patients (155 male/143 female with mean age ± SD of 57 ± 15) were enrolled in the study and categorized into two groups with infarction (n = 142) and without infarction (n = 156). Sixty-three patients (21/1%) had fPCA and 231 (77.5%) had PCoA hypoplasia/aplasia. No significant correlation was identified between fPCA or PCoA hypoplasia/aplasia and presence of infarction. However, regarding the territories involved by infarction, the frequency of thalamus infarction was higher in subgroup with PCoA hypoplasia/aplasia, 17/101 (16.8%) compared to 1/41(2.4%) in the subgroup without ipsilateral PCoA hypoplasia/aplasia (p = 0.024). In two subgroups with and without ipsilateral fPCA variation, frequency of infarction in brain territories was not different significantly. Conclusions: In patients with brain infarction, aplastic/hypoplastic ipsilateral PCoA is associated with higher incidence of thalamic territory infarction.

PURPOSE/UNASSIGNED:To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. MATERIALS AND METHODS/UNASSIGNED:potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. RESULTS/UNASSIGNED:Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model's ability to produce images substantially distinct from the training set, even at 100× acceleration. CONCLUSION/UNASSIGNED:© RSNA, 2022.

Diffuse spinal cord gliomas (SCGs) are rare tumors associated with a high morbidity and mortality that affect both pediatric and adult populations. In this retrospective study, we sought to characterize the clinical, pathological, and molecular features of diffuse SCG in 22 patients with histological and molecular analyses. The median age of our cohort was 23.64 years (range 1-82) and the overall median survival was 397 days. K27M mutation was significantly more prevalent in males compared to females. Gross total resection and chemotherapy were associated with improved survival, compared to biopsy and no chemotherapy. While there was no association between tumor grade, K27M status (p = 0.366) or radiation (p = 0.772), and survival, males showed a trend toward shorter survival. K27M mutant tumors showed increased chromosomal instability and a distinct DNA methylation signature.

The corpus callosum (CC) is the major interhemispheric commissure and its abnormalities include agenesis, hypoplasia, and hyperplasia. The CC anomalies are typically related to other central nervous system (CNS) or extra-CNS malformations. The antenatal diagnosis of complete CC agenesis is easy after mid-trimester by ultrasound (US) even in the axial plane. The non-visualization of cavum septum pellucidum and colpocephaly are critical signs in the axial view. More subtle findings (i.e., hypoplasia and partial agenesis) might also be recognized antenatally. In this review, the focus was given on the prenatal diagnosis of CC abnormalities in US and magnetic resonance imaging.

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 < 0.0001). 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 = 0.002). 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.

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 Verschakelen in this issue.

History Part one of this case appeared 4 months previously and may contain larger images. A 32-year-old woman presented to an ophthalmologist for bilateral blurry vision. She underwent MRI of the brain and orbits, which showed a focal abnormality within the pituitary gland. The patient was referred to an endocrinologist for further evaluation. Review of systems and physical examination by the endocrinologist revealed no symptoms or signs of endocrine dysfunction. Anterior pituitary hormone levels, including growth hormone, prolactin, thyroid stimulating hormone, follicular-stimulating hormone, luteinizing hormone, and adrenocorticotropic hormone, were normal. Dynamic contrast-enhanced MRI of the sella and pituitary gland and subsequent CT of the anterior skull base were performed.

The raccoon roundworm Baylisascaris procyonis (B. procyonis) may infect humans to cause severe or fatal meningoencephalitis, as well as ocular and visceral larva migrans. Young children are at greater risk for cerebral larva migrans with severe meningoencephalitis, and early empiric therapy may improve outcomes. Familiarity with characteristic brain imaging findings may prompt earlier diagnosis, particularly in the setting of CSF eosinophilia. We report a case of a 19-month-old boy who presented with truncal ataxia and was found to have peripheral and CSF eosinophilia. MRI demonstrated symmetric, confluent T2 hyperintense signal in the cerebral and cerebellar deep white mater, which helped differentiate B. procyonis meningoencephalitis from other infectious and non-infectious causes of eosinophilic meningoencephalitis. Early recognition and treatment of B. procyonis meningoencephalitis are important for improved outcomes, and careful review of neuroimaging can play a critical role in suggesting the diagnosis.