Use of artificial intelligence and radio genomics in neuroradiology and the future of brain tumour imaging and surgical planning in low- and middleincome countries.

Brain tumour diagnosis involves assessing various radiological and histopathological parameters. Imaging modalities are an excellent resource for disease monitoring. However, manual inspection of imaging is laborious, and performance varies depending on expertise. Artificial Intelligence (AI) driven solutions a non-invasive and low-cost technology for diagnostics compared to surgical biopsy and histopathological diagnosis. We analysed various machine learning models reported in the literature and assess its applicability to improve neuro-oncological management. A scoping review of 47 full texts published in the last 3 years pertaining to the use of machine learning for the management of different types of gliomas where radiomics and radio genomic models have proven to be useful. Use of AI in conjunction with other factors can result in improving overall neurooncological management within LMICs. AI algorithms can evaluate medical imaging to aid in the early detection and diagnosis of brain tumours. This is especially useful where AI can deliver reliable and efficient screening methods, allowing for early intervention and treatment.

Developmental, Cognitive, Ocular Motor, and Neuroimaging Findings Related to SUFU Haploinsufficiency: Unraveling Subtle and Highly Variable Phenotypes.

BACKGROUND: Biallelic SUFU variants have originally been linked to Joubert syndrome, comprising cerebellar abnormalities, dysmorphism, and polydactyly. In contrast, heterozygous truncating variants have recently been associated with developmental delay and ocular motor apraxia, but only a limited number of patients have been reported. Here, we aim to delineate further the mild end of the phenotypic spectrum related to SUFU haploinsufficiency. METHODS: Nine individuals (from three unrelated families) harboring truncating SUFU variants were investigated, including two previously reported individuals (from one family). We provide results from a comprehensive assessment comprising neuroimaging, neuropsychology, video-oculography, and genetic testing. RESULTS: We identified three inherited or de novo truncating variants in SUFU (NM_016169.4): c.895C>T p.(Arg299∗), c.71dup p.(Ala25Glyfs∗23), and c.71del p.(Pro24Argfs∗72). The phenotypic expression showed high variability both between and within families. Clinical features include motor developmental delay (seven of nine), axial hypotonia (five of nine), ocular motor apraxia (three of nine), and cerebellar signs (three of nine). Four of the six reported children had macrocephaly. Neuropsychological and developmental assessments revealed mildly delayed language development in the youngest children, whereas general cognition was normal in all variant carriers. Subtle but characteristic SUFU-related neuroimaging abnormalities (including superior cerebellar dysplasia, abnormalities of the superior cerebellar peduncles, rostrally displaced fastigium, and vermis hypoplasia) were observed in seven of nine individuals. CONCLUSIONS: Our data shed further light on the mild but recognizable features of SUFU haploinsufficiency and underline its marked phenotypic variability, even within families. Notably, neurodevelopmental and behavioral abnormalities are mild compared with Joubert syndrome and seem to be well compensated over time.

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This work introduces a novel approach towards creating 3-dimensional (3D) medical foundation models for multimodal neuroimage segmentation through self-supervised training. Our approach involves a novel two-stage pretraining approach using vision transformers. The first stage encodes anatomical structures in generally healthy brains from the large-scale unlabeled neuroimage dataset of multimodal brain magnetic resonance imaging (MRI) images from 41,400 participants. This stage of pertaining focuses on identifying key features such as shapes and sizes of different brain structures. The second pretraining stage identifies disease-specific attributes, such as geometric shapes of tumors and lesions and spatial placements within the brain. This dual-phase methodology significantly reduces the extensive data requirements usually necessary for AI model training in neuroimage segmentation with the flexibility to adapt to various imaging modalities. We rigorously evaluate our model, BrainSegFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainSegFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the model complexity and the volume of unlabeled training data derived from generally healthy brains. Both of these factors enhance the accuracy and predictive capabilities of the model in neuroimage segmentation tasks. Our pretrained models and code are at https://github.com/lab-smile/BrainSegFounder.

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans.

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

CalciSeg: A versatile approach for unsupervised segmentation of calcium imaging data.

Recent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand. To overcome these issues, we developed CalciSeg, a data-driven and reproducible approach for unsupervised functional calcium imaging data segmentation. CalciSeg addresses the challenges associated with brain structure variability and user bias by offering a computationally efficient solution for automatic image segmentation based on two parameters: regions' size limits and number of refinement iterations. We evaluated CalciSeg efficacy on datasets of varied complexity, different insect species (locusts, bees, and cockroaches), and imaging systems (wide-field, confocal, and multiphoton), showing the robustness and generality of our approach. Finally, the user-friendly nature and open-source availability of CalciSeg facilitate the integration of this algorithm into existing analysis pipelines.

Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging.

People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol and tobacco use. However, the further associations between alcohol/tobacco use and symptoms/cognition in these disorders remain unexplored. We identified multimodal brain networks involving alcohol use (n = 707) and tobacco use (n = 281) via supervised multimodal fusion and evaluated if these networks affected symptoms and cognition in people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n = 178/134/143), depressive (major depressive disorder, n = 260) and developmental (autism spectrum disorder/attention deficit hyperactivity disorder, n = 421/346) disorders. Alcohol and tobacco use scores were used as references to guide functional and structural imaging fusion to identify alcohol/tobacco use associated multimodal patterns. Correlation analyses between the extracted brain features and symptoms or cognition were performed to evaluate the relationships between alcohol/tobacco use with symptoms/cognition in 6 psychiatric disorders. Results showed that (1) the default mode network (DMN) and salience network (SN) were associated with alcohol use, whereas the DMN and fronto-limbic network (FLN) were associated with tobacco use; (2) the DMN and fronto-basal ganglia (FBG) related to alcohol/tobacco use were correlated with symptom and cognition in psychosis; (3) the middle temporal cortex related to alcohol/tobacco use was associated with cognition in depression; (4) the DMN related to alcohol/tobacco use was related to symptom, whereas the SN and limbic system (LB) were related to cognition in developmental disorders. In summary, alcohol and tobacco use were associated with structural and functional abnormalities in DMN, SN and FLN and had significant associations with cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.

The associations between common neuroimaging parameters of Progressive Supranuclear Palsy in magnetic resonance imaging and non-specific inflammatory factors - pilot study.

Progressive Supranuclear Palsy is an atypical parkinsonism based on tauopathic pathology. Growing interest is associated with the pathomechanism of this disease. Among theories analyzing this issue can be mentioned the one highlighting the significance of inflammation. In this study authors examined 14 patients with PSP-Richardson syndrome (PSP-RS) and 13 healthy volunteers using laboratory testing based on the analysis of interleukins 1 and 6 (IL-1 and IL-6), tau in the cerebrospinal fluid (CSF) and non-specific parameters of peripheral inflammation in the serum (IL-1, IL-6, neutrophils, lymphocytes, monocytes, platelets and the ratios based on the factors). All of the patients underwent neuroimaging using magnetic resonance imaging using 3 Tesla. The serum levels of IL-1 were positively correlated with the area of the mesencephalon, suggesting that higher levels of IL-1 are not linked with atrophic changes in this region, whereas serum levels IL-6 was positively correlated with frontal horn width and negatively correlated with superior cerebellar area. Additionally IL-6 in the serum was found to be correlated with neutrophil-to-high density lipoprotein ratio. The observations were not confirmed in the analysis of the levels of interleukins in the CSF. To the best of our knowledge this work is one of the first analyzing this issue. The outcome of the work shows that the role of interleukins associated with microglial activation may possibly differ in the context of neurodegenerative changes, moreover the role of peripheral inflammation in PSP requires further analysis.

Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder.

The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.

Association between Cerebrospinal Fluid sTREM2 Levels and Depression: The Alzheimer's Disease Neuroimaging Initiative Study.

OBJECTIVE: Previous studies demonstrated a significant protective effect of elevated cerebrospinal fluid (CSF) sTREM2 levels on brain structure and cognitive decline. Nonetheless, the role of sTREM2 in the depression progression remains unclear. This study aimed to investigate the association between CSF sTREM2 levels and longitudinal trajectories of depression. METHODS: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) Study were used. CSF sTREM2 levels and depression were measured using an ELISA-based assay and the Geriatric Depression Scale (GDS-15), respectively. Linear mixed-effect models were employed to assess the relationships between CSF sTREM2 levels and GDS scores. RESULTS: A total of 1,017 participants were enrolled at baseline, with a mean follow-up time of 4.65 years. Baseline CSF sTREM2 levels were negatively correlated with GDS scores (ß=-0.21, P=0.022) after adjustment for age, gender, race/ethnicity, education, APOE ε4 carrier status, TREM2 rare variant carrier status, marital status, smoking, and clinical cognitive status. CONCLUSION: Our findings suggested that a higher level of CSF sTREM2 was associated with a lower risk of depression.

The Role of Glia in Wilson's Disease: Clinical, Neuroimaging, Neuropathological and Molecular Perspectives.

Wilson's disease (WD) is inherited in an autosomal recessive manner and is caused by pathogenic variants of the ATP7B gene, which are responsible for impaired copper transport in the cell, inhibition of copper binding to apoceruloplasmin, and biliary excretion. This leads to the accumulation of copper in the tissues. Copper accumulation in the CNS leads to the neurological and psychiatric symptoms of WD. Abnormalities of copper metabolism in WD are associated with impaired iron metabolism. Both of these elements are redox active and may contribute to neuropathology. It has long been assumed that among parenchymal cells, astrocytes have the greatest impact on copper and iron homeostasis in the brain. Capillary endothelial cells are separated from the neuropil by astrocyte terminal legs, putting astrocytes in an ideal position to regulate the transport of iron and copper to other brain cells and protect them if metals breach the blood-brain barrier. Astrocytes are responsible for, among other things, maintaining extracellular ion homeostasis, modulating synaptic transmission and plasticity, obtaining metabolites, and protecting the brain against oxidative stress and toxins. However, excess copper and/or iron causes an increase in the number of astrocytes and their morphological changes observed in neuropathological studies, as well as a loss of the copper/iron storage function leading to macromolecule peroxidation and neuronal loss through apoptosis, autophagy, or cuproptosis/ferroptosis. The molecular mechanisms explaining the possible role of glia in copper- and iron-induced neurodegeneration in WD are largely understood from studies of neuropathology in Parkinson's disease and Alzheimer's disease. Understanding the mechanisms of glial involvement in neuroprotection/neurotoxicity is important for explaining the pathomechanisms of neuronal death in WD and, in the future, perhaps for developing more effective diagnostic/treatment methods.

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has shown promise in improving accuracy. However, the lack of transparency in AI decision-making processes presents a challenge for clinical adoption. METHODS: Recent advancements in deep learning, particularly the utilization of CNNs, have facilitated the development of models for medical image analysis. In this study, we employed the EfficientNetB0 architecture and integrated explainable AI techniques to enhance both accuracy and interpretability. Grad-CAM visualization was utilized to highlight significant areas in MRI scans influencing classification decisions. RESULTS: Our model achieved a classification accuracy of 98.72 % across four categories of brain tumors (Glioma, Meningioma, No Tumor, Pituitary), with precision and recall exceeding 97 % for all categories. The incorporation of explainable AI techniques was validated through visual inspection of Grad-CAM heatmaps, which aligned well with established diagnostic markers in MRI scans. CONCLUSION: The AI-enhanced EfficientNetB0 framework with explainable AI techniques significantly improves brain tumor classification accuracy to 98.72 %, offering clear visual insights into the decision-making process. This method enhances diagnostic reliability and trust, demonstrating substantial potential for clinical adoption in medical diagnostics.

Occult Amyloid-ß-Related Angiitis: Neuroimaging Findings at 1.5T, 3T, and 7T MRI.

Cerebral amyloid angiopathy (CAA) is a progressive neurodegenerative small vessel disease that is associated with intracranial hemorrhage and cognitive impairment in the elderly. The clinical and radiographic presentations have many overlapping features with vascular cognitive impairment, hemorrhagic stroke, and Alzheimer disease (AD). Amyloid-ß-related angiitis (ABRA) is a form of primary CNS vasculitis linked to CAA, with the development of spontaneous autoimmune inflammation against amyloid in the vessel wall with resultant vasculitis. The diagnosis of ABRA and CAA is important. ABRA is often fatal if untreated and requires prompt immunosuppression. Important medical therapies such as anticoagulation and antiamyloid agents for AD are contraindicated in CAA. Here, we present a biopsy-proved case of ABRA with underlying occult CAA. Initial 1.5T and 3T MR imaging did not suggest CAA per the Boston Criteria 2.0. ABRA was not included in the differential diagnosis due to the lack of any CAA-related findings on conventional MR imaging. However, a follow-up 7T MR imaging revealed extensive cortical/subcortical cerebral microbleeds, cortical superficial siderosis, and intragyral hemorrhage in extensive detail throughout the supratentorial brain regions, which radiologically supported the diagnosis of ABRA in the setting of CAA. This case suggests an increased utility of high-field MR imaging to detect occult hemorrhagic neuroimaging findings with the potential to both diagnose more patients with CAA and diagnose them earlier.

Radiomics and artificial intelligence applications in pediatric brain tumors.

BACKGROUND: The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity and mortality. While both morphological and non-morphological magnetic resonance imaging techniques can give important information concerning tumor characterization, grading, and patient prognosis, increasing evidence in recent years has highlighted the need for personalized treatment and the development of quantitative imaging parameters that can predict the nature of the lesion and its possible evolution. For this purpose, radiomics and the use of artificial intelligence software, aimed at obtaining valuable data from images beyond mere visual observation, are gaining increasing importance. This brief review illustrates the current state of the art of this new imaging approach and its contributions to understanding CNS tumors in children. DATA SOURCES: We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: ("radiomics" AND/OR "artificial intelligence") AND ("pediatric AND brain tumors"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected. RESULTS: A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The "radiomic workflow" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model. CONCLUSIONS: In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out.

Progress and trends in neurological disorders research based on deep learning.

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Automated segmentation of epilepsy surgical resection cavities: Comparison of four methods to manual segmentation.

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n = 44, 88 %) and Epic-CHOP (n = 42, 84 %) were able to detect more resection cavities than Resseg (n = 22, 44 %, P < 0.001) and Deep Resection (n = 23, 46 %, P < 0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts. In the temporal cohort, the SPM-based pipelines had higher detection rates, however there was no difference in the accuracy between methods. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

Effects of antidepressants on brain structure and function in patients with obsessive-compulsive disorder: A review of neuroimaging studies.

Obsessive-compulsive disorder (OCD) affects 2-3% of people worldwide. Although antidepressants are the standard pharmachological treatment of OCD, their effect on the brain of individuals with OCD has not yet been fully clarified. We conducted a systematic search on PubMed, Scopus, Embase, and Web of Science to explore the effects of antidepressants on neuroimaging findings in OCD. Thirteen neuroimaging investigations were included. After antidepressant treatment, structural magnetic resonance imaging studies suggested thalamic, amygdala, and pituitary volume changes in patients. In addition, the use of antidepressants was associated with alterations in diffusion tensor imaging metrics in the left striatum, the right midbrain, and the posterior thalamic radiation in the right parietal lobe. Finally, functional magnetic resonance imaging highlighted possible changes in the ventral striatum, frontal, and prefrontal cortex. The small number of included studies and sample sizes, short durations of follow-up, different antidepressants, variable regions of interest, and heterogeneous samples limit the robustness of the findings of the present review. In conclusion, our review suggests that antidepressant treatment is associated with brain changes in individuals with OCD, and these results may help to deepen our knowledge of the pathophysiology of OCD and the brain mechanisms underlying the effects of antidepressants.

Neuroradiological features of contrast-induced neurotoxicity: A systematic review and pooled analysis.

BACKGROUND: Contrast-induced neurotoxicity (CIN), is an increasingly recognised complication of endovascular procedures, presenting as a spectrum of neurological symptoms that mimic ischaemic stroke. The diagnosis of CIN remains a clinical challenge, and stereotypical imaging findings are not established. This study was conducted to characterise the neuroimaging findings in patients with CIN, to raise diagnostic awareness and improve decision making. METHODS: We performed a systematic review of PubMed and Embase databases from inception (1946/1947) to June 2023 for reports of CIN following administration of iodinated contrast media. Studies with a final diagnosis of CIN, which provided details of neuroimaging were included. All included cases were pooled and descriptive analysis was conducted. RESULTS: A total of 84 patients were included, with a median age of 64 years. A large proportion of patients had normal imaging (CT 40.8 %, MRI 53.1 %). CT abnormalities included cortical/subarachnoid hyperattenuation (42.1 %), cerebral oedema/sulcal effacement (26.3 %), and loss of grey-white differentiation (7.9 %). Frequently reported MRI abnormalities included brain parenchymal MRI signal change (40.8 %) and cerebral oedema (12.2 %), most commonly observed on FLAIR sequences (26.5 %). Characterisation of imaging findings according to anatomical location and clinical symptoms has been conducted. CONCLUSIONS: Neuroimaging is an essential part of the diagnostic workup of CIN. Analysis of the anatomical location and laterality of imaging abnormalities may suggest relationship between radiological features and actual clinical symptoms, although this remains to be confirmed with dedicated study. Radiological abnormalities, particularly CT, appear to be transient and reversible in most patients.

Best Evidence Topic report: Is a CT head required for patients who present to the emergency department with a first seizure?

A short systematic review was undertaken to assess whether adult patients presenting to the ED with a first seizure require a CT head scan to rule out emergent intracranial pathology. MEDLINE, EMBASE, Cochrane and Google Scholar databases were searched. Seven relevant papers were identified. Study information, patient characteristics, key results and methodological weaknesses were tabulated. Our results indicate that adults presenting with a first seizure are a high-yield group for CT with a number needed to scan (NNS) between 10 and 19 for findings that would change management in ED, such as haemorrhage, infarction and tumours. We believe that this NNS is sufficiently low to justify the routine use of neuroimaging for these patients in emergency care.

Role of neuroimaging markers on predicting of idiopathic intracranial hypertension.

BACKGROUND: The goals of neuroimaging in idiopathic intracranial hypertension (IIH) are the exclusion of mimickers and effective management of disease. In recent studies, several imaging markers have been identified as potential predictors of IIH. PURPOSE: To investigate the predictive roles of novel radiological markers as the Meckel's cave area, alongside classical radiologic markers in identifying IIH such as the empty sella. MATERIAL AND METHODS: The patients were classified according to cerebrospinal fluid (CSF) opening pressure as the IIH group and control group. The observational, case-control study included 22 patients with IIH and 22 controls. Groups were compared for presence of empty sella, Meckel's cave area, fat area of posterior neck, fat thickness of scalp, presence of transverse sinus stenosis, and ophthalmic markers, such as increase of optic nerve (ON) sheath diameter. RESULTS: In the IHH group, higher occurrences of increased ON sheath diameter, ON tortuosity, flattening of the scleral surface, and transverse sinus stenosis were observed (P < 0.001, P < 0.001, P = 0.046, and P = 0.021, respectively). Meckel's cave area and fat area of posterior neck were similar in both groups (P = 0.444 and P = 0.794). CONCLUSION: Ophthalmic markers and transverse sinus stenosis could be utilized as radiologic features supporting early and precise diagnosis of IIH. However, enlargement of Meckel's cave area and measurements of fatty area of posterior neck are not helpful for diagnosis of IIH.