Purpose:To implement a magnetic resonance (MR) imaging protocol to measure intracranial atherosclerotic disease (ICAD) in a population-based multicenter study and report examination and reader reliability of these MR imaging measurements and descriptive statistics representative of the general population. Materials and Methods:This prospective study was approved by the institutional review boards and compliant with HIPAA. Conclusion:Vessel wall MR imaging is a reliable tool for identifying and measuring ICAD and provided insight into ICAD distribution across a U.S. community-based population.q RSNA, 2016

IMPORTANCE Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke. OBJECTIVES To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods. DESIGN, SETTING, AND PARTICIPANTS In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019. MAIN OUTCOMES AND MEASURES Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of Ն0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion. RESULTS Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, −14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance. CONCLUSIONS AND RELEVANCE The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

The aim of this study was to investigate the prevalence of interleukin (IL)-17-producing CD4+ T cells (Th17) and regulatory T (Treg) cells in children with primary nephrotic syndrome. The study cohort consisted of 62 children who were randomly divided into control, primary nephrotic syndrome, and isolated hematuria groups. Flow cytometric analysis revealed the presence of Th17 cells in the peripheral blood mononuclear cells (PBMCs) of 35 children and Tregs in the PBMCs of all children. In addition, mRNA expression of Th17-related factors [IL-17, -23p19 and retinoid orphan nuclear receptor (RORc)] and the concentration of plasma inflammatory mediators such as IL-6 and IL-1beta were consistently detected in all children. Protein expression of IL-17 and transforming growth factor-beta1 were also detected in renal biopsy tissue and compared between different groups. Patients with PNS were found to have an increased number of Th17 cells and decreased numbers of Tregs in their PBMCs, and there was significant difference in the prevalence of Th17 and Tregs between the patients with PNS and those with isolated hematuria. Our data show that among our study cohort, there was a dynamic equilibrium between Th17 and Treg cells in children with PNS following the development of PNS with apparent renal tubular epithelial cell and interstitium lesions. The dynamic interaction between Th17 and Treg cells may be important in the development of PNS.

Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.

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