Cerebellar Microstructural Abnormalities in Obsessive-Compulsive Disorder (OCD): a Systematic Review of Diffusion Tensor Imaging Studies.

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.

Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.

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.

Cerebellar Microstructural Abnormalities in Parkinson's Disease: a Systematic Review of Diffusion Tensor Imaging Studies.

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.

Clinical applications of diffusion-weighted sequence in brain imaging: beyond stroke.

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.

Leveraging artificial intelligence in ischemic stroke imaging.

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.

Cerebral Venous Sinus Thrombosis following Ventriculoperitoneal Shunting for Idiopathic Intracranial Hypertension.

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.

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

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.

The Role of Medical Imaging in COVID-19.

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.

Misleading CT perfusion in subacute ischemic stroke.

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.

SUMOylation in Glioblastoma: A Novel Therapeutic Target.

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.

Clinical application of PET/MRI in oncology.

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.

Effect of spatial resolution of T2-weighted imaging on diagnostic efficacy of MRI in detection of papilledema.

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.

Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform.

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.

Burden of Ischemic and Hemorrhagic Stroke Across the US From 1990 to 2019.

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 100 000 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.

Magnetic Resonance Imaging (MRI) Findings in COVID-19 Associated Encephalitis.

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.

Subcortical signal alteration of corticospinal tracts. A radiologic manifestation of ARIA: A case report.

Patients with Alzheimer's disease who have been given monoclonal antibodies targeting amyloid-ß (Aß) (eg, gantenerumab, donanemab, lecanemab, and aducanumab) for scientific purposes may have a spectrum of imaging findings known as amyloid-related imaging abnormalities (ARIA), shown on brain magnetic resonance imaging (MRI) scans. These neuroimaging abnormalities are caused by antibody-mediated destruction of accumulated Aß aggregates in cerebral blood vessels and brain parenchyma. ARIA may demonstrate as brain edema or sulcal effusion (ARIA-E) or as hemosiderin deposits caused by brain parenchymal or pial hemorrhage (ARIA-H). The current study explores 2 cases with interval development of FLAIR hyper signal intensity along the bilateral corticospinal tracts in the motor cortex/precentral gyri after treatment by aducanumab. We believe this manifestation is a subtype of ARIA-A that has not been explored earlier. Our first case was a 72-year-old woman with a history of HTN and kidney transplant (polycystic kidney) who presented with mild cognitive impairment with clinical findings consistent with early Alzheimer's disease. After receiving 3 doses of aducunumab and experiencing cognition improvement, she underwent a brain MRI because of dizziness and vertigo. The brain MRI demonstrated new FLAIR hyper signal intensity in subcortical regions of precentral gyri (motor cortex) symmetrically as well as trace subarachnoid hemorrhage at the vertex compatible with ARIA-E and ARIA-H. Our second case was an 85-year-old woman with a history of small lymphocytic leukemia which was treated 20 years earlier. After orthopedic surgery 2 years ago, she developed dementia with anterograde amnesia. Since then, Aricept and Namenda have been started, but there have been no improvements in her subjective condition. The initial Amyloid PET/MR imaging showed diffuse cerebral Amyloid deposition. After tolerating 6 doses of aducanumab a safety MRI revealed new bilateral symmetric FLAIR hyper signal intensity in the subcortical motor cortex. Results of our study suggest that the subcortical corticospinal tract is another hotspot for ARIA findings. Hence, these regions might be an unknown site for both the action and adverse effects of aducanumab on amyloid plaques with secondary inflammation. In addition, radiologists must take this phenomenon into the account, and be cognizant that the FLAIR hyper signal intensities should not be misinterpreted as motor neuron disease (eg, amyotrophic lateral sclerosis).

Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models.

Background: Pulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes. Materials and methods: In this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality. Results: A total number of 1,017 patients were finally enrolled in the study, including 465 women and 552 men. Overall incidence of study main endpoint was 9.6%, (7.2% in men and 12.4% in women; p-value = 0.05). The overall performance of the GB model is better than the other two models (AUC: 0.94 for GB vs. 0.88 and 0.90 for DL and LR models respectively). Based on GB model, lower O2 saturation and right ventricle dilation and dysfunction were among the strongest adverse event predictors. Conclusion: ML-based models have notable prediction ability in PE patients. These algorithms may help physicians to detect high-risk patients earlier and take appropriate preventive measures.