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Previous neuroimaging studies have suggested that obsessive-compulsive disorder (OCD) is associated with altered resting-state functional connectivity of the cerebellum. In this study, we aimed to describe the most significant and reproducible microstructural abnormalities and cerebellar changes associated with obsessive-compulsive disorder (OCD) using diffusion tensor imaging (DTI) investigations. PubMed and EMBASE were searched for relevant studies using the PRISMA 2020 protocol. A total of 17 publications were chosen for data synthesis after screening titles and abstracts, full-text examination, and executing the inclusion criteria. The patterns of cerebellar white matter (WM) integrity loss, determined by fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) metrics, varied across studies and symptoms. Changes in fractional anisotropy (FA) values were described in six publications, which were decreased in four and increased in two studies. An increase in diffusivity parameters of the cerebellum (i.e., MD, RD, and AD) in OCD patients was reported in four studies. Alterations of the cerebellar connectivity with other brain areas were also detected in three studies. Heterogenous results were found in studies that investigated cerebellar microstructural abnormalities in correlation with symptom dimension or severity. OCD's complex phenomenology may be characterized by changes in cerebellar WM connectivity across wide networks, as shown by DTI studies on OCD patients in both children and adults. Classification features in machine learning and clinical tools for diagnosing OCD and determining the prognosis of the disorder might both benefit from using cerebellar DTI data.
Subject(s)
Obsessive-Compulsive Disorder , White Matter , Adult , Child , Humans , Diffusion Tensor Imaging/methods , Brain/diagnostic imaging , Diffusion Magnetic Resonance Imaging/methods , White Matter/diagnostic imaging , Obsessive-Compulsive Disorder/diagnostic imaging , AnisotropyABSTRACT
BACKGROUND: Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. METHODS AND MATERIALS: A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. RESULTS: We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. CONCLUSION: MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
Subject(s)
Alzheimer Disease , Cognitive Dysfunction , Neurodegenerative Diseases , Humans , Alzheimer Disease/diagnosis , Cognitive Dysfunction/diagnosis , Magnetic Resonance Imaging/methods , Sensitivity and SpecificityABSTRACT
Diffusion tensor imaging (DTI) is now having a strong momentum in research to evaluate the neural fibers of the CNS. This technique can study white matter (WM) microstructure in neurodegenerative disorders, including Parkinson's disease (PD). Previous neuroimaging studies have suggested cerebellar involvement in the pathogenesis of PD, and these cerebellum alterations can correlate with PD symptoms and stages. Using the PRISMA 2020 framework, PubMed and EMBASE were searched to retrieve relevant articles. Our search revealed 472 articles. After screening titles and abstracts, and full-text review, and implementing the inclusion criteria, 68 papers were selected for synthesis. Reviewing the selected studies revealed that the patterns of reduction in cerebellum WM integrity, assessed by fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity measures can differ symptoms and stages of PD. Cerebellar diffusion tensor imaging (DTI) changes in PD patients with "postural instability and gait difficulty" are significantly different from "tremor dominant" PD patients. Freezing of the gate is strongly related to cerebellar involvement depicted by DTI. The "reduced cognition," "visual disturbances," "sleep disorders," "depression," and "olfactory dysfunction" are not related to cerebellum microstructural changes on DTI, while "impulsive-compulsive behavior" can be linked to cerebellar WM alteration. Finally, higher PD stages and longer disease duration are associated with cerebellum white matter alteration depicted by DTI. Depiction of cerebellar white matter involvement in PD is feasible by DTI. There is an association with disease duration and severity and several clinical presentations with DTI findings. This clinical-imaging association may eventually improve disease management.
Subject(s)
Parkinson Disease , White Matter , Anisotropy , Diffusion Tensor Imaging/methods , Humans , Neuroimaging , Parkinson Disease/pathology , White Matter/diagnostic imaging , White Matter/pathologyABSTRACT
Diffusion-weighted imaging (DWI) is a well-established MRI sequence for diagnosing early stroke and provides therapeutic implications. However, DWI yields pertinent information in various other brain pathologies and helps establish a specific diagnosis and management of other central nervous system disorders. Some of these conditions can present with acute changes in neurological status and mimic stroke. This review will focus briefly on diffusion imaging techniques, followed by a more comprehensive description of the utility of DWI in common neurological entities beyond stroke.
Subject(s)
Brain Ischemia , Stroke , Brain/diagnostic imaging , Diffusion Magnetic Resonance Imaging , Humans , Magnetic Resonance Imaging , Stroke/diagnostic imagingABSTRACT
Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
Subject(s)
Ischemic Stroke , Stroke , Artificial Intelligence , Diagnostic Imaging , Humans , Ischemic Stroke/diagnostic imaging , Machine Learning , Stroke/diagnostic imagingABSTRACT
A 30-year-old woman with idiopathic intracranial hypertension experienced worsening headaches and decreasing vision in her left eye. She underwent an uncomplicated ventriculoperitoneal shunt procedure but the following day was found to have cerebral venous sinus thrombosis. Treatment included venous sinus thrombectomy and anticoagulation. She had a favourable clinical outcome. Extensive evaluation including testing for thrombophilia was unremarkable. Potential causes for this rare association are discussed.
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INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS: Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS: By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION: The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.
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Glioma/diagnostic imaging , Machine Learning , Magnetic Resonance Imaging/methods , Precision Medicine/methods , Glioma/pathology , Humans , Neoplasm Grading , Retrospective Studies , SoftwareABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic launched in the third decade of the twenty-first century and continued to present time to cause the worst challenges the modern medicine has ever encountered. Medical imaging is an essential part of the universal fight against this pandemic. In the absence of documented treatment and vaccination, early accurate diagnosis of infected patients is the backbone of this pandemic management. This chapter reviews different aspects of medical imaging in the context of COVID-19.
Subject(s)
COVID-19 , Humans , Pandemics , Radiography, Thoracic , SARS-CoV-2 , Tomography, X-Ray ComputedABSTRACT
The RAPID© software is the most commonly used computed tomography perfusion (CTP) software in stroke centers. It is estimated that about 1300 hospitals in the world are using this software for decision-making in ischemic stroke. The software provides the estimated volume of infarction and ischemic penumbra, so it is the backbone of treatment planning in these patients. In this manuscript, we present two cases of subacute infarction with misleading CTP using RAPID© software. We believe that given the popularity of this software and increasing application of CTP in subacute infarction, this pitfall is likely underdiagnosed in many patients. In a subacute phase of infarction, we recommend diffusion-weighted imaging magnetic resonance imaging (DWI-MRI) for estimation of infarction to avoid this pitfall and possible mismanagement.
Subject(s)
Brain Ischemia/diagnostic imaging , Stroke/diagnostic imaging , Tomography, X-Ray Computed/methods , Brain Ischemia/therapy , Computed Tomography Angiography , Conservative Treatment , Diagnosis, Differential , Humans , Male , Middle Aged , Radiographic Image Interpretation, Computer-Assisted , Software , Stroke/therapyABSTRACT
Protein SUMOylation is a dynamic post-translational modification which is involved in a diverse set of physiologic processes throughout the cell. Of note, SUMOylation also plays a role in the pathobiology of a myriad of cancers, one of which is glioblastoma (GBM). Accordingly, herein, we review core aspects of SUMOylation as it relates to GBM and in so doing highlight putative methods/modalities capable of therapeutically engaging the pathway for treatment of this deadly neoplasm.
Subject(s)
Antineoplastic Agents/pharmacology , Brain Neoplasms/drug therapy , Brain Neoplasms/metabolism , Glioblastoma/drug therapy , Glioblastoma/metabolism , Sumoylation/drug effects , Animals , Antineoplastic Agents/therapeutic use , Brain/drug effects , Brain/metabolism , Brain/pathology , Brain Neoplasms/pathology , Glioblastoma/pathology , Humans , Molecular Targeted Therapy/methods , Protein Processing, Post-Translational/drug effects , Small Ubiquitin-Related Modifier Proteins/metabolismSubject(s)
Ocular Motility Disorders , Orbital Myositis , Humans , Orbital Myositis/diagnosis , Face , Eye , Ocular Motility Disorders/diagnosisABSTRACT
Hybrid imaging with integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) combines the advantages of the high-resolution anatomic data from MRI and functional imaging data from PET, and has the potential to improve the diagnostic evaluation of various types of cancers. The clinical oncologic applications of this newest hybrid imaging technology are evolving and substantial efforts are underway to define the role of PET/MRI in routine clinical use. The current published literature suggests that PET/MRI may play an important role in the evaluation of patients with certain types of malignancies, involving anatomic locations such as the pelvis and the liver. The purpose of this article is to review the current published PET/MRI literature in specific body oncologic applications. In addition, PET/MRI protocols and some of the technical issues of this hybrid imaging will be briefly discussed. J. Magn. Reson. Imaging 2016;44:265-276.
Subject(s)
Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Multimodal Imaging/methods , Neoplasms/diagnostic imaging , Neoplasms/pathology , Positron-Emission Tomography/methods , Animals , Humans , Image Enhancement/methods , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
OBJECTIVE. Morphologic changes associated with papilledema may be masked by partial volume averaging effects in images obtained at a slice thickness greater than normal optic nerve thickness. We aimed to compare the diagnostic accuracy of high-resolution 3D T2-weighted imaging performed at submillimeter slice thickness with conventional T2-weighted imaging performed at 5-mm slice thickness for detection of papilledema. MATERIALS AND METHODS. Two blinded neuroradiologists evaluated conventional and high-resolution axial T2-weighted imaging across orbits from 25 patients with clinically proven papilledema and 66 control participants without papilledema. They graded optic nerve sheath distention and optic nerve head configuration, also making a binary determination for presence or absence of papilledema for each set of images. The diagnostic accuracy of each technique was assessed in terms of sensitivity, specificity, positive likelihood ratio, and interobserver agreement. These parameters were compared using the homogeneity of odds ratio and McNemar tests. RESULTS. High-resolution T2-weighted imaging was associated with higher sensitivity (83.3% vs 56.2%, p = 0.0072 for reader 1; 87.5% vs 54.2% for reader 2, p = 0.0001) but unchanged specificity. High-resolution T2-weighted imaging was significantly better than conventional T2-weighted imaging in detecting optic nerve head deformity in patients with papilledema, but there was no difference between two techniques in detection of optic nerve sheath distention. High-resolution imaging also enabled greater interobserver agreement (κ = 0.82) compared with conventional T2-weighted image (κ = 0.62). CONCLUSION. Improved visualization of the optic nerve head afforded by high-resolution T2-weighted imaging translates into better diagnostic performance of MRI in detection of papilledema, with higher sensitivity and interobserver reliability.
Subject(s)
Imaging, Three-Dimensional , Magnetic Resonance Imaging/methods , Papilledema/diagnosis , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , Young AdultABSTRACT
Magnetic resonance imaging (MRI) of the brain has been implemented to evaluate multiple intracranial pathologies. Non-contrast T2-weighted images are a routinely acquired sequence in almost all neuroimaging protocols. It is not uncommon to encounter various cerebrovascular lesions incidentally on brain imaging. Neuroradiologists should evaluate the routine T2-weighted images for incidental cerebrovascular lesions, irrespective of the primary indication of the study. Vascular structures typically demonstrate a low signal flow-void on the T2-weighted images. In our experience, large cerebrovascular abnormalities are easily visible to a typical neuroradiologist. In this article, we present the spectrum of the characteristic imaging appearance of various intracranial cerebrovascular lesions on routine non-contrast T2-weighted MRI. These include aneurysm, arteriovenous malformation, arterial occlusion, capillary telangiectasia, cavernous malformation, dural arteriovenous fistula, moyamoya, proliferative angiopathy, and vein of Galen malformation.
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Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen-Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists' scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists' ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists' scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists' scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland-Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform's low inter-user variability and the high accuracy of its T1c and FLAIR AI models.
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Background: Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. Methods: The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Results: Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99. Discussion: CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.
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Importance: Stroke is a leading cause of death and disability in the US. Accurate and updated measures of stroke burden are needed to guide public health policies. Objective: To present burden estimates of ischemic and hemorrhagic stroke in the US in 2019 and describe trends from 1990 to 2019 by age, sex, and geographic location. Design, Setting, and Participants: An in-depth cross-sectional analysis of the 2019 Global Burden of Disease study was conducted. The setting included the time period of 1990 to 2019 in the US. The study encompassed estimates for various types of strokes, including all strokes, ischemic strokes, intracerebral hemorrhages (ICHs), and subarachnoid hemorrhages (SAHs). The 2019 Global Burden of Disease results were released on October 20, 2020. Exposures: In this study, no particular exposure was specifically targeted. Main Outcomes and Measures: The primary focus of this analysis centered on both overall and age-standardized estimates, stroke incidence, prevalence, mortality, and DALYs per 100â¯000 individuals. Results: In 2019, the US recorded 7.09 million prevalent strokes (4.07 million women [57.4%]; 3.02 million men [42.6%]), with 5.87 million being ischemic strokes (82.7%). Prevalence also included 0.66 million ICHs and 0.85 million SAHs. Although the absolute numbers of stroke cases, mortality, and DALYs surged from 1990 to 2019, the age-standardized rates either declined or remained steady. Notably, hemorrhagic strokes manifested a substantial increase, especially in mortality, compared with ischemic strokes (incidence of ischemic stroke increased by 13% [95% uncertainty interval (UI), 14.2%-11.9%]; incidence of ICH increased by 39.8% [95% UI, 38.9%-39.7%]; incidence of SAH increased by 50.9% [95% UI, 49.2%-52.6%]). The downturn in stroke mortality plateaued in the recent decade. There was a discernible heterogeneity in stroke burden trends, with older adults (50-74 years) experiencing a decrease in incidence in coastal areas (decreases up to 3.9% in Vermont), in contrast to an uptick observed in younger demographics (15-49 years) in the South and Midwest US (with increases up to 8.4% in Minnesota). Conclusions and Relevance: In this cross-sectional study, the declining age-standardized stroke rates over the past 3 decades suggest progress in managing stroke-related outcomes. However, the increasing absolute burden of stroke, coupled with a notable rise in hemorrhagic stroke, suggests an evolving and substantial public health challenge in the US. Moreover, the significant disparities in stroke burden trends across different age groups and geographic locations underscore the necessity for region- and demography-specific interventions and policies to effectively mitigate the multifaceted and escalating burden of stroke in the country.
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We conducted this study to investigate the scope of the MRI neuroimaging manifestations in COVID-19-associated encephalitis. From January 2020 to September 2021, patients with clinical diagnosis of COVID-19-associated encephalitis, as well as concomitant abnormal imaging findings on brain MRI, were included. Two board-certified neuro-radiologists reviewed these selected brain MR images, and further discerned the abnormal imaging findings. 39 patients with the clinical diagnosis of encephalitis as well as abnormal MRI findings were included. Most (87%) of these patients were managed in ICU, and 79% had to be intubated-ventilated. 15 (38%) patients died from the disease, while the rest were discharged from the hospital. On MRI, FLAIR hyperintensities in the insular cortex were the most common finding, seen in 38% of the patients. Micro-hemorrhages on the SWI images were equally common, also seen in 38% patients. FLAIR hyperintensities in the medial temporal lobes were seen in 30%, while FLAIR hyperintensities in the posterior fossa were evident in 20%. FLAIR hyperintensities in basal ganglia and thalami were seen in 15%. Confluent FLAIR hyperintensities in deep and periventricular white matter, not explained by microvascular angiopathy, were detected in 7% of cases. Cortical-based FLAIR hyperintensities in 7%, and FLAIR hyperintensity in the splenium of the corpus callosum in 7% of patients. Finally, isolated FLAIR hyperintensity around the third ventricle was noted in 2% of patients.