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Query: UMLS:C0038454 (stroke)
 147,016 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Introduction: Leukodystrophies constitute heterogenous group of rare heritable disorders primarily affecting the white matter of central nervous system. These conditions are often under-appreciated among physicians. The first clinical manifestations of leukodystrophies are often nonspecific and can occur in different ages from neonatal to late adulthood periods. The diagnosis is, therefore, challenging in most cases.Area covered: Herein, the authors discuss different aspects of leukodystrophies. The authors used MEDLINE, EMBASE, and GOOGLE SCHOLAR to provide an extensive update about epidemiology, classifications, pathology, clinical findings, diagnostic tools, and treatments of leukodystrophies. Comprehensive evaluation of clinical findings, brain magnetic resonance imaging, and genetic studies play the key roles in the early diagnosis of individuals with leukodystrophies. No cure is available for most heritable white matter disorders but symptomatic treatments can significantly decrease the burden of events. New genetic methods and stem cell transplantation are also under investigation to further increase the quality and duration of life in affected population.Expert opinion: The improvements in molecular diagnostic tools allow us to identify the meticulous underlying etiology of leukodystrophies and result in higher diagnostic rates, new classifications of leukodystrophies based on genetic information, and replacement of symptomatic managements with more specific targeted therapies.Abbreviations: 4H: Hypomyelination, hypogonadotropic hypogonadism and hypodontia; AAV: Adeno-associated virus; AD: autosomal dominant; AGS: Aicardi-Goutieres syndrome; ALSP: Axonal spheroids and pigmented glia; APGBD: Adult polyglucosan body disease; AR: autosomal recessive; ASO: Antisense oligonucleotide therapy; AxD: Alexander disease; BAEP: Brainstem auditory evoked potentials; CAA: Cerebral amyloid angiopathy; CADASIL: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASAL: Cathepsin A-related arteriopathy with strokes and leukoencephalopathy; CARASIL: Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy; CGH: Comparative genomic hybridization; ClC2: Chloride Ion Channel 2; CMTX: Charcot-Marie-Tooth disease, X-linked; CMV: Cytomegalovirus; CNS: central nervous system; CRISP/Cas9: Clustered regularly interspaced short palindromic repeat/CRISPR-associated 9; gRNA: Guide RNA; CTX: Cerebrotendinous xanthomatosis; DNA: Deoxyribonucleic acid; DSB: Double strand breaks; DTI: Diffusion tensor imaging; FLAIR: Fluid attenuated inversion recovery; GAN: Giant axonal neuropathy; H-ABC: Hypomyelination with atrophy of basal ganglia and cerebellum; HBSL: Hypomyelination with brainstem and spinal cord involvement and leg spasticity; HCC: Hypomyelination with congenital cataracts; HEMS: Hypomyelination of early myelinated structures; HMG CoA: Hydroxy methylglutaryl CoA; HSCT: Hematopoietic stem cell transplant; iPSC: Induced pluripotent stem cells; KSS: Kearns-Sayre syndrome; L-2-HGA: L-2-hydroxy glutaric aciduria; LBSL: Leukoencephalopathy with brainstem and spinal cord involvement and elevated lactate; LCC: Leukoencephalopathy with calcifications and cysts; LTBL: Leukoencephalopathy with thalamus and brainstem involvement and high lactate; MELAS: Mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke; MERRF: Myoclonic epilepsy with ragged red fibers; MLC: Megalencephalic leukoencephalopathy with subcortical cysts; MLD: metachromatic leukodystrophy; MRI: magnetic resonance imaging; NCL: Neuronal ceroid lipofuscinosis; NGS: Next generation sequencing; ODDD: Oculodentodigital dysplasia; PCWH: Peripheral demyelinating neuropathy-central-dysmyelinating leukodystrophy-Waardenburg syndrome-Hirschprung disease; PMD: Pelizaeus-Merzbacher disease; PMDL: Pelizaeus-Merzbacher-like disease; RNA: Ribonucleic acid; TW: T-weighted; VWM: Vanishing white matter; WES: whole exome sequencing; WGS: whole genome sequencing; X-ALD: X-linked adrenoleukodystrophy; XLD: X-linked dominant; XLR: X-linked recessive.
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PMID:An update on clinical, pathological, diagnostic, and therapeutic perspectives of childhood leukodystrophies. 3182 48

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the model's architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.
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PMID:Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks. 3242 23

Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic lesion from multislice non-contrast CT (NCCT) volumes of AIS patients. This paper proposes a 2D dense multi-path contextual generative adversarial network (MPC-GAN) where a dense multi-path 2D U-Net is utilized as the generator and a discriminator network is applied to regularize the generator. Contextual information (i.e. bilateral intensity difference, distance map and lesion location probability) are input into the generator and discriminator. The proposed method is validated separately on follow-up NCCT volumes of 60 patients with ischemic infarcts and NCCT volumes of 70 patients with hemorrhages. Quantitative results demonstrated that the proposed MPC-GAN method obtained a Dice coefficient (DC) of 70.6% for ischemic infarct segmentation and a DC of 76.5% for hemorrhage segmentation compared with manual segmented lesions, outperforming several benchmark methods. Additional volumetric analyses demonstrated that the MPC-GAN segmented lesion volume correlated well with manual measurements (Pearson correlation coefficients were 0.926 and 0.927 for ischemic infarcts and hemorrhages, respectively). The proposed MPC-GAN method can accurately segment ischemic infarcts and hemorrhages from NCCT volumes of AIS patients.
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PMID:Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network. 3260 80