Imaging Assessment of Fetal Cerebrovascular Anomalies.

Four distinct vascular anomalies can be seen to affect the brain on fetal imaging: vein of Galen malformations, non-galenic arteriovenous pial fistulas, dural sinus malformations, and intracranial venous malformations. These congenital disorders affect the arteries and veins of the developing brain and are rarely seen beyond the neonatal stage. The four fetal cerebrovascular anomalies are associated with quite disparate natural histories and prognoses. MRI plays a pivotal role in the evaluation of fetuses with these conditions due to its ability to definitively establish the diagnosis, to detect subtle parenchymal injuries, to delineate the course of abnormal vessels in detail and to some extent the nature of vascular flow, and to identify ischemic, thrombotic, and hemorrhagic complications. Recently, an investigational transurterine embolization procedure targeted at treating fetuses with vein of Galen malformations who are at high risk for neonatal decompensation has emerged as a promising alternative to expectant management and post-natal embolization, with imaging being used to identify suitable patients for the intervention and in pre-procedural planning. This manuscript reviews the essential imaging and clinical features of these four fetal neurovascular anomalies and underscores the practical aspects related to counseling, prognosis, and the multidisciplinary management of these entities.ABBREVIATIONS: ACVRL1= activin A receptor like type 1; b-SSFP=Balanced Steady State Free precession; DSM= Dural Sinus Malformation; Ephrin B4= Ephrin type-B receptor 4; icVM= Intracranial Venous Malformation; ITGB1= Integrin Subunit Beta 1; NOTCH1= Neurogenic locus notch homolog protein 1; PTPN11= Protein Tyrosine Phosphatase Non-Receptor Type 11; RASA1= RAS P21 Protein Activator 1; SSFSE= Single-shot fast spin echo; VOGM=Vein of Galen Malformation.

Imaging of pediatric skull lytic lesions: A review.

Skull lesions in pediatric population are common findings on imaging and sometimes with heterogeneous manifestations, constituting a diagnostic challenge. Some lesions can be misinterpreted for their aggressiveness, as with larger lesions eroding cortical bone, containing soft tissue components, leading to excessive and, in some cases, invasive inappropriate etiological investigation. In this review, we present multiple several conditions that may present as skull lesions or pseudolesions, organized by groups (anatomic variants, congenital and development disorders, traumatic injuries, vascular issues, infectious conditions, and tumoral processes). Anatomic variants are common imaging findings that must be recognized by the neuroradiologist. Congenital malformations are rare conditions, such as aplasia cutis congenita and sinus pericranii, usually seen at earlier ages, the majority of which are benign findings. In case of trauma, cephalohematoma, growing skull fractures, and posttraumatic lytic lesions should be considered. Osteomyelitis tends to be locally aggressive and may mimic malignancy, in which cases, the clinical history can be the key to diagnosis. Vascular (sickle cell disease) and tumoral (aneurismal bone cyst, eosinophilic granuloma, metastases) lesions are relatively rare lesions but should be considered in the differential diagnosis, in the presence of certain imaging findings. The main difficulty is the differentiation between the benign and malignant nature; therefore, the main objective of this pictorial essay is to review the main skull lytic lesions found in pediatric age, describing the main findings in different imaging modalities (CT and MRI), allowing the neuroradiologist greater confidence in establishing the differential diagnosis, through a systematic and simple characterization of the lesions.

Brain Spectroscopy Analysis in Retired Soccer Players With Chronic Exposure to Mild Traumatic Brain Injuries.

Soccer players are at risk of suffering cranial injuries in the short and long term. There is growing concern that this may lead to traumatic brain injury in soccer players. Magnetic resonance spectroscopy (MRS) is an analytical method that enables the measurement of changes in brain metabolites that usually occur before significant structural changes. This study aimed to use MRS to compare variations in brain metabolite levels between retired soccer players and a control group. Twenty retired professional soccer players and 22 controls underwent magnetic resonance imaging, including MRS sequences and Mini-Mental State Examination (MMSE). Metabolite analysis was conducted based on absolute concentration and relative ratios. N-acetyl-aspartate, choline, glutamate, glutamine, and myoinositol were the metabolites of interest for the statistical analysis. Retired soccer players had an average age of 57.8 years, whereas the control group had an average age of 63.2 years. Median cognitive evaluation score, assessed using the MMSE, was 28 [26-29] for athletes and 29 [28-30] for controls (p = 0.01). Uni- and multi-variate analyses of the absolute concentration of metabolites (mM) between former athletes and controls did not yield any statistically significant results. Comparison of metabolites to creatine ratio concentrations did not yield any statistically significant results. There were no changes in concentrations of brain metabolites that indicated brain metabolic changes in retired soccer players compared with controls.

Brain MRI Abnormalities, Epilepsy and Intellectual Disability in LAMA2 Related Dystrophy - a Genotype/Phenotype Correlation.

BACKGROUND: LAMA2-related muscular dystrophy is a disorder that causes muscle weakness and varies in severity, from a severe, congenital type to a milder, late-onset form. However, the disease does not only affect the muscles, but has systemic involvement and can lead to alterations such as brain malformation, epilepsy and intellectual disability. OBJECTIVE: Describe the frequency of cortical malformations, epilepsy and intellectual disability in LAMA2-RD in a Brazilian cohort and correlate the neurological findings to genetic and motor function. METHODS: This is an observational study of 52 LAMA2-RD patients, who were divided into motor function subgroups and compared based on brain MRI findings, epilepsy, intellectual disability, and type of variants and variant domains. RESULTS: 44 patients (84.6%) were only able to sit, and 8 patients (15.4%) were able to walk. 10 patients (19.2%) presented with cortical malformations (polymicrogyria, lissencephaly-pachygyria, and cobblestone),10 patients (19.2%) presented with epilepsy, and 8 (15.4%) had intellectual disability. CNS manifestations correlated with a more severe motor phenotype and none of the patients able to walk presented with cortical malformation or epilepsy. There was a relation between gene variants affecting the laminin-α2 LG-domain and the presence of brain malformation (P = 0.016). There was also a relation between the presence of null variants and central nervous system involvement. A new brazilian possible founder variant was found in 11 patients (21,15%) (c.1255del; p. Ile419Leufs*4). CONCLUSION: Cortical malformations, epilepsy and intellectual disability are more frequent among LAMA2-RD patients than previously reported and correlate with motor function severity and the presence of variants affecting the laminin-α2 LG domain. This brings more insight fore phenotype-genotype correlations, shows the importance of reviewing the brain MRI of patients with LAMA2-RD and allows greater attention to the risk of brain malformation, epilepsy, and intellectual disability in those patients with variants that affect the LG domain.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This article illustrates some of these applications. This article reviews machine learning challenges related to neuroradiology. The first approval of reimbursement for an AI algorithm by the Centers for Medicare and Medicaid Services, covering a stroke software for early detection of large vessel occlusion, is also discussed.

A Deep Learning-based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience.

PURPOSE: To develop a deep learning model for detecting brain abnormalities on MR images. MATERIALS AND METHODS: In this retrospective study, a deep learning approach using T2-weighted fluid-attenuated inversion recovery images was developed to classify brain MRI findings as "likely normal" or "likely abnormal." A convolutional neural network model was trained on a large, heterogeneous dataset collected from two different continents and covering a broad panel of pathologic conditions, including neoplasms, hemorrhages, infarcts, and others. Three datasets were used. Dataset A consisted of 2839 patients, dataset B consisted of 6442 patients, and dataset C consisted of 1489 patients and was only used for testing. Datasets A and B were split into training, validation, and test sets. A total of three models were trained: model A (using only dataset A), model B (using only dataset B), and model A + B (using training datasets from A and B). All three models were tested on subsets from dataset A, dataset B, and dataset C separately. The evaluation was performed by using annotations based on the images, as well as labels based on the radiology reports. RESULTS: Model A trained on dataset A from one institution and tested on dataset C from another institution reached an F1 score of 0.72 (95% CI: 0.70, 0.74) and an area under the receiver operating characteristic curve of 0.78 (95% CI: 0.75, 0.80) when compared with findings from the radiology reports. CONCLUSION: The model shows relatively good performance for differentiating between likely normal and likely abnormal brain examination findings by using data from different institutions.Keywords: MR-Imaging, Head/Neck, Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021Supplemental material is available for this article.

Cerebellofaciodental syndrome in an adult patient: Expanding the phenotypic and natural history characteristics.

Cerebellofaciodental syndrome is characterized by facial dysmorphisms, intellectual disability, cerebellar hypoplasia, and dental anomalies. It is an autosomal-recessive condition described in 2015 caused by pathogenic variants in BRF1. Here, we report a Brazilian patient who faced a diagnostic challenge beginning at 11 months of age. Fortunately, whole-exome sequencing (WES) was performed, detecting the BRF1 variants NM_001519.3:c.1649delG:p.(Gly550Alafs*36) and c.421C>T:p.(Arg141Cys) in compound heterozygosity, thus finally achieving a diagnosis of cerebellofaciodental syndrome. The patient is currently 25 years old and is the oldest patient yet reported. The clinical report and a review of published cases are presented. Atlanto-occipital fusion, a reduced foramen magnum and basilar invagination leading to compression of the medulla-spinal cord transition are skeletal findings not reported in previous cases. The description of syndromes with dental findings shows that such anomalies can be an important clue to relevant differential diagnoses. The cooperation of groups from different international centers made possible the resolution of this and other cases and is one of the strategies to bring medical advances to developing countries, where many patients with rare diseases are difficult to diagnose definitively.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

PURPOSE: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. MATERIALS AND METHODS: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. RESULTS: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. CONCLUSIONS: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

Brain abnormalities in myelomeningocele patients.

BACKGROUND: Myelomeningocele (MMC) is often related to hydrocephalus and Chiari malformation (CM) type 2; however, other brain abnormalities have been reported in this population. In order to better understand and quantify other forebrain abnormalities, we analyzed magnetic resonance imaging (MRI) of MMC patients treated in utero or postnatal. METHODS: Between January 2014 and March 2017, 59 MMC were treated in our hospital. Thirty-seven patients (32 postnatal and 5 intrautero repair) had brain MRI and were enrolled at the study. MRI was analyzed by two experienced neuroradiologists to identify the supra and infratentorial brain abnormalities. RESULTS: A wide range of brain abnormalities was consistently identified in MMC patients. As expected, the most common were hydrocephalus (94.5%) and CM type II (89.1%). Of note, we found high incidence of corpus callosum abnormalities (86.4%), mostly represented by dysplasia (46%). CONCLUSIONS: The data are consistent with the concept that brain abnormalities related to MMC can be both infratentorial and supratentorial, cortical, and subcortical. More studies are needed to correlate these forebrain abnormalities to long-term functional outcome and their prognostic value for these patients.