Objective To describe the clinical features of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy in children. Method Data from 11 pediatric patients with autoimmune GFAP astrocytopathy were retrospectively analyzed. Results All of the patients showed encephalitis and meningoencephalitis or meningoencephalomyelitis with or without myelitis. 45.4% of the patients had fever, 27.3% headaches, 18.2% dizziness, 18.2% drowsiness, and 18.2% mental disorders. Cerebrospinal fluid (CSF) was detected in all patients. The white blood cell counts (WBC) (90.9%), lactic dehydrogenase levels (72.7%), protein level (36.4%), and adenosine deaminase activity (ADA) level (27.3%) were elevated, and the CSF glucose levels (72.7%) were slightly reduced. Nine patients (90%) were found to have brain abnormalities, of which five (50.0%) patients had abnormal symmetrical laminar patterns or line patterns hyperintensity lesions on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images in the basal ganglia, hypothalamus, subcortical white matter and periventricular white matter. The linear radial enhancement pattern of the cerebral white matter was only seen in two patients, with the most common being abnormal enhancement of leptomeninges (50%). Five patients had longitudinally extensive spinal cord lesions. Conclusion The findings of pediatric patients with autoimmune GFAP astrocytopathy are different from previous reports.

ObjectiveThe deep medullary veins (DMVs) can be evaluated using susceptibility-weighted imaging (SWI). This study aimed to apply radiomic analysis of the DMVs to evaluate brain injury in neonatal patients with hypoxic-ischemic encephalopathy (HIE) using SWI.MethodsThis study included brain magnetic resonance imaging of 190 infants with HIE and 89 controls. All neonates were born at full-term (37+ weeks gestation). To include the DMVs in the regions of interest, manual drawings were performed. A Rad-score was constructed using least absolute shrinkage and selection operator (LASSO) regression to identify the optimal radiomic features. Nomograms were constructed by combining the Rad-score with a clinically independent factor. Receiver operating characteristic curve analysis was applied to evaluate the performance of the different models. Clinical utility was evaluated using a decision curve analysis.ResultsThe combined nomogram model incorporating the Rad-score and clinical independent predictors, was better in predicting HIE (in the training cohort, the area under the curve was 0.97, and in the validation cohort, it was 0.95) and the neurologic outcomes after hypoxic-ischemic (in the training cohort, the area under the curve was 0.91, and in the validation cohort, it was 0.88).ConclusionBased on radiomic signatures and clinical indicators, we developed a combined nomogram model for evaluating neonatal brain injury associated with perinatal asphyxia.

Neonatal subpial hemorrhage is a poorly understood type of intracranial hemorrhage. Herein, we reported on 34 neonates with subpial hemorrhages, focusing on the imaging features, clinical factors, and outcomes of this type of intracranial hemorrhage. This retrospective case series enrolled 34 neonates with subpial hemorrhages. We analyzed their magnetic resonance (MR) images, clinical manifestations, and prognoses. We categorized, for the first time, the MR images of patients with subpial hemorrhages into three imaging patterns; moreover, on the basis of a yin-yang sign, we added a sandwich sign, attaining an MR image feature that was easier to understand. MR Patterns A and B both have good prognoses, and most patients had normal clinical outcomes. Subpial hemorrhage in neonates may be diagnosed via imaging patterns. Recognizing this pattern of hemorrhage may help gain a better understanding of the associated risk factors.

Objective This study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates. Methods A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interest (ROI). For the purpose of identifying optimal radiomics features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomics features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility. Results A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomics model and the combined nomogram model was 0.84 and 0.84. For the validation cohort, the AUC of the radiomics model and the combined nomogram model was 0.80 and 0.79, respectively. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group. Conclusion We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.

ObjectiveThis study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates. Methods A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interests (ROI). For the purpose of identifying optimal radiomic features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomic features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility. Results A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomic model, and the combined nomogram model were 0.84, 0.84. For the validation cohort, the AUC of the radiomic model, and the combined nomogram model were 0.80, 0.79. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group. Conclusion We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.