Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions.

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

Neuroimaging Features of Cytokine-related Diseases.

Cytokines are small secreted proteins that have specific effects on cellular interactions and are crucial for functioning of the immune system. Cytokines are involved in almost all diseases, but as microscopic chemical compounds they cannot be visualized at imaging for obvious reasons. Several imaging manifestations have been well recognized owing to the development of cytokine therapies such as those with bevacizumab (antibody against vascular endothelial growth factor) and chimeric antigen receptor (CAR) T cells and the establishment of new disease concepts such as interferonopathy and cytokine release syndrome. For example, immune effector cell-associated neurotoxicity is the second most common form of toxicity after CAR T-cell therapy toxicity, and imaging is recommended to evaluate the severity. The emergence of COVID-19, which causes a cytokine storm, has profoundly impacted neuroimaging. The central nervous system is one of the systems that is most susceptible to cytokine storms, which are induced by the positive feedback of inflammatory cytokines. Cytokine storms cause several neurologic complications, including acute infarction, acute leukoencephalopathy, and catastrophic hemorrhage, leading to devastating neurologic outcomes. Imaging can be used to detect these abnormalities and describe their severity, and it may help distinguish mimics such as metabolic encephalopathy and cerebrovascular disease. Familiarity with the neuroimaging abnormalities caused by cytokine storms is beneficial for diagnosing such diseases and subsequently planning and initiating early treatment strategies. The authors outline the neuroimaging features of cytokine-related diseases, focusing on cytokine storms, neuroinflammatory and neurodegenerative diseases, cytokine-related tumors, and cytokine-related therapies, and describe an approach to diagnosing cytokine-related disease processes and their differentials. ©RSNA, 2024 Supplemental material is available for this article.

[Advances of pathological research and classification in malformations of cortical development associated with refractory epilepsy].

With rapid development of genetic testing techniques, neuroimaging and neuroelectrophysiological technologies, our understanding of malformations of cortical development continues to be deepened and updated. In particular, mutations in genes related to the mammalian target of rapamycin (mTOR) signaling pathway have been successively discovered in focal cortical dysplasia (FCD). At the same time, the classification consensus on FCD issued by the International League Against Epilepsy (ILAE) in 2011 has encountered problems and challenges in diagnostic practice. Therefore, in 2022, ILAE proposed an updated version of the FCD classification based on the progress in molecular genetics over the past decade. The main addition to the classification system is "white matter lesions, " and it is also suggested to integrate histopathological, neuroimaging, and molecular testing results for multi-level integrated diagnosis to achieve reliable, clinically relevant, and therapeutic targeted final diagnosis.

Use of Recommended Neurodiagnostic Evaluation Among Patients With Drug-Resistant Epilepsy.

Importance: Interdisciplinary practice parameters recommend that patients with drug-resistant epilepsy (DRE) undergo comprehensive neurodiagnostic evaluation, including presurgical assessment. Reporting from specialized centers suggests long delays to referral and underuse of surgery; however, longitudinal data are limited to characterize neurodiagnostic evaluation among patients with DRE in more diverse US settings and populations. Objective: To examine the rate and factors associated with neurodiagnostic studies and comprehensive evaluation among patients with DRE within 3 US cohorts. Design, Setting, and Participants: A retrospective cross-sectional study was conducted using the Observational Medical Outcomes Partnership Common Data Model including US multistate Medicaid data, commercial claims data, and Columbia University Medical Center (CUMC) electronic health record data. Patients meeting a validated computable phenotype algorithm for DRE between January 1, 2015, and April 1, 2020, were included. No eligible participants were excluded. Exposure: Demographic and clinical variables were queried. Main Outcomes and Measures: The proportion of patients receiving a composite proxy for comprehensive neurodiagnostic evaluation, including (1) magnetic resonance or other advanced brain imaging, (2) video electroencephalography, and (3) neuropsychological evaluation within 2 years of meeting the inclusion criteria. Results: A total of 33 542 patients with DRE were included in the Medicaid cohort, 22 496 in the commercial insurance cohort, and 2741 in the CUMC database. A total of 31 516 patients (53.6%) were women. The proportion of patients meeting the comprehensive evaluation main outcome in the Medicaid cohort was 4.5% (n = 1520); in the commercial insurance cohort, 8.0% (n = 1796); and in the CUMC cohort, 14.3% (n = 393). Video electroencephalography (24.9% Medicaid, 28.4% commercial, 63.2% CUMC) and magnetic resonance imaging of the brain (35.6% Medicaid, 43.4% commercial, 52.6% CUMC) were performed more regularly than neuropsychological evaluation (13.0% Medicaid, 16.6% commercial, 19.2% CUMC) or advanced imaging (3.2% Medicaid, 5.4% commercial, 13.1% CUMC). Factors independently associated with greater odds of evaluation across all 3 data sets included the number of inpatient and outpatient nonemergency epilepsy visits and focal rather than generalized epilepsy. Conclusions and Relevance: The findings of this study suggest there is a gap in the use of diagnostic studies to evaluate patients with DRE. Care setting, insurance type, frequency of nonemergency visits, and epilepsy type are all associated with evaluation. A common data model can be used to measure adherence with best practices across a variety of observational data sources.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Natural Fat Nanoemulsions for Enhanced Optical Coherence Tomography Neuroimaging and Tumor Imaging in the Second Near-Infrared Window.

Optical coherence tomography (OCT) imaging mainly uses backscattered light to visualize the structural and functional information on biological tissues. In particular, OCT angiography can not only map the capillary networks but also capture the blood flow in the tissue microenvironment, making it a good candidate for neuroimaging and tumor imaging in vivo and in real time. To further improve the detection accuracy of cancer or brain disorders, it is essential to develop a natural and nontoxic contrast agent for enhanced OCT imaging in the second near-infrared (NIR-II) window. In this study, a superior biocompatible and highly scattering NIR-II fat nanoemulsion was constructed to improve OCT imaging contrast and depth for monitoring the vascular network changes of the cerebral cortex or tumor. In vivo experimental results demonstrated that a natural fat nanoemulsion can serve as an excellent probe for enhanced OCT neuroimaging and tumor imaging.

Malignancy-associated ischemic stroke: Implications for diagnostic and therapeutic workup.

BACKGROUND: Patients with malignancies have an increased risk of suffering ischemic stroke via several mechanisms such as coagulation dysfunction and other malignancy-related effects as well as iatrogenic causes. Moreover, stroke can be the first sign of an occult malignancy, termed as malignancy-associated ischemic stroke (MAS). Therefore, timely diagnostic assessment and targeted management of this complex clinical situation are critical. FINDINGS: Patients with both stroke and malignancy have atypical ages, risk factors, and often exhibit malignancy-related symptoms and multiple lesions on neuroimaging. New biomarkers such as eicosapentaenoic acid and blood mRNA profiles may help in distinguishing MAS from other strokes. In terms of treatment, malignancy should not be considered a contraindication, given comparable rates of recanalization and complications between stroke patients with or without malignancies. CONCLUSION: In this review, we summarize the latest developments in diagnosing and managing MAS, especially stroke with occult malignancies, and provide new recommendations from recently emerged clinical evidence for diagnostic and therapeutic workup strategies.

Enhancing Diagnostic Accuracy Through Neuroimaging Revisions in Pediatric Pseudotumor Cerebri Syndrome: A Cross-Sectional Study.

BACKGROUND: This cross-sectional study aimed to report all neuroimaging findings suggestive of raised intracranial pressure in children with pseudotumor cerebri syndrome (PTCS), before and after re-review by two neuroradiologists. METHODS: We included 48 children aged <18 years diagnosed with PTCS between 2016 and 2021. Clinical and radiological data were obtained from their medical files. Two neuroradiologists independently re-reviewed all neuroimages, and the average of their assessments was compared with the initial neuroimaging reports; an additional review was done to analyze inter- and intraclass correlation. RESULTS: The initial neuroimaging reports showed under-reporting of findings, with only 26 of 48 (54.1%) patients identified with abnormal reports. After revision, the proportion of the reported findings increased to 44 of 48 (91.6%). Distention of the perioptic space was the most commonly reported finding after revision (36.5 of 48; 76%). Flattening of the posterior globe and empty sella were initially under-reported but improved after revision. Moreover, several findings suggestive of increased intracranial pressure not mandated by Friedman criteria were identified, such as narrowing of the Meckel cave, posterior displacement of the pituitary stalk, and narrowing of the cavernous sinus. Analysis of associations between neuroimaging findings and demographic and clinical characteristics yielded no statistically significant results. The inter- and intraclass correlation results demonstrated a significant agreement between raters and within each rater's assessment (P < 0.05). CONCLUSIONS: This study highlights the impact of image revision in enhancing PTCS diagnosis. Intra- and interclass correlations underscore the reliability of the review process, emphasizing the importance of meticulous image analysis in clinical practice.

Brain imaging derived phenotypes: a biomarker for the onset of inflammatory bowel disease and a potential mediator of mental complications.

Background and aims: Inflammatory bowel disease (IBD), mainly categorized into Crohn's disease (CD) and ulcerative colitis (UC), is a chronic relapsing gastrointestinal disorder that significantly impairs patients' quality of life. IBD patients often experience comorbidities such as anxiety and depression, and the underlying mechanisms and treatment strategies remain areas of investigation. Methods: We conducted a Mendelian randomization(MR) analysis utilizing brain image derived phenotypes (IDP) from the UK Biobank database to investigate the causal relationships between IBD and alterations in brain structural morphology and connectivity of neural tracts. This study aimed to identify biological evidence linking IBD to psychiatric disorders such as anxiety and depression. Results: Specifically, the volume of grey matter in the Left Frontal Orbital Cortex exhibited a negative association with the onset of Crohn's disease (odds ratio (OR) [95% confidence interval (CI)]: 0.315[0.180~0.551], adjusted P=0.001), while the volume of the superior frontal cortex in the right hemisphere showed a positive correlation with the development of Ulcerative colitis (OR [95% CI]: 2.285[1.793~2.911], adjusted P<0.001), and the volume of lateral occipital cortex in the left hemisphere demonstrated a positive relationship with Crohn's disease onset (OR [95% CI]: 1.709[1.671~1.747], adjusted P<0.001). In the context of reverse causality, the onset of UC or CD has led to alterations in imaging derived phenotypes associated with five disorders (anxiety, depression, schizophrenia, bipolar disorder, pain) and three functions (memory, emotion, language). Conclusion: Our study has demonstrated a causal relationship between IBD and IDPs. IDPs may serve as potential biomarkers for the progression of IBD and as predictive intermediaries for the development of neurological diseases in IBD patients.

Combining neuroimaging and brain stimulation to test alternative causal pathways for nicotine addiction in schizophrenia.

The smoking rate is high in patients with schizophrenia. Brain stimulation targeting conventional brain circuits associated with nicotine addiction has also yielded mixed results. We aimed to identify alternative circuitries associated with nicotine addiction in both the general population and schizophrenia, and then test whether modulation of such circuitries may alter nicotine addiction behaviors in schizophrenia. In Study I of 40 schizophrenia smokers and 51 non-psychiatric smokers, cross-sectional neuroimaging analysis identified resting state functional connectivity (rsFC) between the dorsomedial prefrontal cortex (dmPFC) and multiple extended amygdala regions to be most robustly associated with nicotine addiction severity in healthy controls and schizophrenia patients (p = 0.006 to 0.07). In Study II with another 30 patient smokers, a proof-of-concept, patient- and rater-blind, randomized, sham-controlled rTMS design was used to test whether targeting the newly identified dmPFC location may causally enhance the rsFC and reduce nicotine addiction in schizophrenia. Although significant interactions were not observed, exploratory analyses showed that this dmPFC-extended amygdala rsFC was enhanced by 4-week active 10Hz rTMS (p = 0.05) compared to baseline; the severity of nicotine addiction showed trends of reduction after 3 and 4 weeks (p ≤ 0.05) of active rTMS compared to sham; Increased rsFC by active rTMS predicted reduction of cigarettes/day (R = -0.56, p = 0.025 uncorrected) and morning smoking severity (R = -0.59, p = 0.016 uncorrected). These results suggest that the dmPFC-extended amygdala circuit may be linked to nicotine addiction in schizophrenia and healthy individuals, and future efforts targeting its underlying pathophysiological mechanisms may yield more effective treatment for nicotine addiction.

Brain iron concentration in childhood ADHD: A systematic review of neuroimaging studies.

Iron deficiency may play a role in the pathophysiology of Attention Deficit/Hyperactivity Disorder (ADHD). Due to its preponderant function in monoamine catecholamine and myelin synthesis, brain iron concentration may be of primary interest in the investigation of iron dysregulation in ADHD. This study reviewed current evidence of brain iron abnormalities in children and adolescents with ADHD using magnetic resonance imaging methods, such as relaxometry and quantitative susceptibility mapping, to assess brain iron estimates. The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed for studies published between January 1, 2008 and July 7, 2023 in Medline, Scopus and Proquest. Regions of interest, brain iron index values and phenotypical information were extracted from the relevant studies. Risk of bias was assessed using a modified version of the National Heart, Lung, and Blood Institute quality assessment tool. Seven cross-sectional studies comparing brain iron estimates in children with ADHD with neurotypical children were included. Significantly reduced brain iron content in medication-naïve children with ADHD was a consistent finding. Two studies found psychostimulant use may increase and normalize brain iron concentration in children with ADHD. The findings were consistent across the studies despite differing methodologies and may lay the early foundation for the recognition of a potential biomarker in ADHD, although longitudinal prospective neuroimaging studies using larger sample sizes are required. Lastly, the effects of iron supplementation on brain iron concentration in children with ADHD need to be elucidated.

Plasmatic profiles of cytokines/chemokines, glial fibrillary acidic protein (GFAP) and MRI brain damage in neonates with hypoxic ischemic encephalopathy (HIE).

BACKGROUND: Perinatal hypoxia triggers the release of cytokines and chemokines by neurons, astrocytes and microglia. In response to hypoxia-ischemia resting/ramified microglia proliferate and undergo activation, producing proinflammatory molecules. The brain damage extension seems to be related to both the severity of hypoxia and the balance between pro and anti-inflammatory response and can be explored with neuroimaging. AIMS: The aim of this preliminary study was to explore possible relationships between plasma levels of inflammatory cytokines/chemokines and the severe brain damage detectable by Magnetic Resonance Imaging (MRI), performed during the hospitalization. METHODS: In 10 full terms neonates with hypoxic ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH), divided into cases and controls, according to MRI results, we measured and compared the plasma levels of CCL2/MCP-1, CXCL8, GFAP, IFN y, IL-10, IL-18, IL-6, CCL3, ENOLASE2, GM-CSF, IL-1b, IL-12p70, IL-33, TNFα, collected at four different time points during TH (24, 25-48, 49-72 h of life, and 7-10 days from birth). Five of enrolled babies had pathological brain MRI (cases) and 5 had a normal MRI examination (controls). Cytokines were measured by Magnetic Luminex Assay. MRI images were classified according to Barkovich's score. RESULTS: Mean levels of all cytokines and molecules at time T1 were not significantly different in the two groups. Comparing samples paired by day of collection, the greatest differences between cases and controls were found at times T2 and T3, during TH. At T4, levels tended to get closer again (except for IL-6, IL10 and IL18). Infants with worse MRI showed higher plasmatic GFAP levels than those with normal MRI, while their IL-18 was lower. The mean levels of CCL3MIP1alpha, GMCSF, IL1BETA overlapped throughout the observation period in both groups. CONCLUSION: In a small number of infants with worse brain MRI, we found higher levels of GFAP and of IL-10 at T4 and a trend toward low IL-18 levels than in infants with normal MRI, considered early biomarker of brain damage and a predictor of adverse outcome, respectively. The greatest, although not significant, difference between the levels of molecules was found in cases and controls at time points T2 and T3, during TH.

Not Dandy Walker variant: a review of prominent retrocerebellar CSF space in children.

The prominent retrocerebellar cerebrospinal fluid (CSF) space can be frequently encountered on paediatric neuroimaging studies. In cases involving abnormal vermian development where imaging does not align with the established criteria of Dandy-Walker malformation (DWM), the term "Dandy-Walker variant or continuum" has been historically employed to describe the aberrant posterior fossa development. Instead, the emphasis is on a more elaborate description of the findings in the posterior fossa. Moreover, combining the findings in the supratentorial brain can occasionally predict certain neurogenetic disorders that mimic Dandy-Walker phenotype. The present review demonstrates and differentiates the imaging features of various entities that result in an enlarged retrocerebellar CSF space, such as inferior vermian hypoplasia (IVH) and several neurogenetic conditions.

Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach.

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.

Imaging of supratentorial intraventricular masses in children:a pictorial review- part 1.

PURPOSE: This article is the first in a two-part series designed to provide a comprehensive overview of the range of supratentorial intraventricular masses observed in children. Our primary objective is to discuss the diverse types of intraventricular masses that originate not only from cells within the choroid plexus but also from other sources. METHODS: In this article, we review relevant epidemiological data, the current genetics/molecular classification as outlined in the fifth edition of the World Health Organization's Classification of tumours of the Central Nervous System and noteworthy imaging findings. We conduct an exhaustive analysis of primary choroid plexus tumours as well as other conditions such as choroid plexus hyperplasia, choroid plexus cyst, choroid plexus xanthogranuloma, atypical teratoid rhabdoid tumour, meningioma, arteriovenous malformation and metastasis. RESULTS: We comprehensively evaluated each supratentorial intraventricular mass, providing an in-depth analysis of their unique clinical and histological characteristics. The fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System introduces major modifications. These important changes could potentially have a profound impact on the management strategies and subsequent outcomes of these tumours. CONCLUSION: Intraventricular masses in children can arise from various sources. Surgical intervention is key for certain supratentorial intraventricular masses in paediatric patients, with preoperative neuroimaging essential to decide the best treatment approach, surgical or otherwise, as some cases may not require surgery.

Development and Characterization of a Novel Carbon-11 Labeled Positron Emission Tomography Radiotracer for Neuroimaging of Sirtuin 1 with Benzoxazine-Based Compounds.

Introduction: Sirtuins (SIRTs) comprise a group of histone deacetylase enzymes crucial for regulating metabolic pathways and contributing significantly to various disease mechanisms. Sirtuin 1 (SIRT1), among the seven known mammalian homologs, is extensively investigated and understood, playing a key role in neurodegenerative disorders and cancer. This study focuses on potential as a therapeutic target for conditions such as Parkinson's disease (PD), Huntington's disease (HD), and Alzheimer's disease (AD). Methods: Utilizing positron emission tomography (PET) as a noninvasive molecular imaging modality, we aimed to expedite the validation of a promising sirtuin 1 inhibitor for clinical trials. However, the absence of a validated sirtuin 1 PET radiotracer impedes clinical translation. We present the development of [11C]1, and 11C-labeled benzoxazine-based derivative, as a lead imaging probe. The radiosynthesis of [11C]1 resulted in a radiochemical yield of 31 ± 4%. Results: Baseline studies demonstrated that [11C]1 exhibited excellent blood-brain barrier (BBB) penetration capability, with uniform accumulation throughout various brain regions. Self-blocking studies revealed that introducing an unlabeled compound 1, effectively blocking sirtuin 1, led to a substantial reduction in whole-brain uptake, emphasizing the in vivo specificity of [11C]1 for sirtuin 1. Discussion: The development of [11C]1 provides a valuable tool for noninvasive imaging investigations in rodent models with aberrant sirtuin 1 expression. This novel radiotracer holds promise for advancing our understanding of sirtuin 1's role in disease mechanisms and may facilitate the validation of sirtuin 1 inhibitors in clinical trials.

Texture Estimation for Abnormal Tissue Segmentation in Brain MRI.

This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics. Segmentation of the tissues is performed using a feature-based classification method. The efficacy of the mBm texture feature in segmenting different abnormal tissues is demonstrated using a large-scale publicly available clinical dataset. Experimental results and performance of the methods confirm the efficacy of the proposed technique in an automatic segmentation of abnormal tissues in multimodal (T1, T2, Flair, and T1contrast) brain MRIs.

Sparse Dynamic Volume TransUNet with multi-level edge fusion for brain tumor segmentation.

3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and treatment. Accurate segmentation results are critical for localization and spatial distribution of brain tumors using 3D MRI. However, most existing methods mainly focus on extracting global semantic features from the spatial and depth dimensions of a 3D volume, while ignoring voxel information, inter-layer connections, and detailed features. A 3D brain tumor segmentation network SDV-TUNet (Sparse Dynamic Volume TransUNet) based on an encoder-decoder architecture is proposed to achieve accurate segmentation by effectively combining voxel information, inter-layer feature connections, and intra-axis information. Volumetric data is fed into a 3D network consisting of extended depth modeling for dense prediction by using two modules: sparse dynamic (SD) encoder-decoder module and multi-level edge feature fusion (MEFF) module. The SD encoder-decoder module is utilized to extract global spatial semantic features for brain tumor segmentation, which employs multi-head self-attention and sparse dynamic adaptive fusion in a 3D extended shifted window strategy. In the encoding stage, dynamic perception of regional connections and multi-axis information interactions are realized through local tight correlations and long-range sparse correlations. The MEFF module achieves the fusion of multi-level local edge information in a layer-by-layer incremental manner and connects the fusion to the decoder module through skip connections to enhance the propagation ability of spatial edge information. The proposed method is applied to the BraTS2020 and BraTS2021 benchmarks, and the experimental results show its superior performance compared with state-of-the-art brain tumor segmentation methods. The source codes of the proposed method are available at https://github.com/SunMengw/SDV-TUNet.

PAEDIATRIC SYMPTOMATIC SEIZURES IN INDIA: UNRAVELLING VARIED ETIOLOGIES AND NEUROIMAGING PATTERNS - A MULTICENTRIC STUDY.

Pediatric neuroimaging presents a unique set of challenges, primarily stemming from the intricacies of normal myelination processes occurring within the initial two years of life. This complexity is particularly pronounced in the context of pediatric epilepsy, where a substantial proportion of neuroimaging cases appears normal, especially in instances of idiopathic or provoked seizures. Nevertheless, abnormalities in neuroimaging tend to manifest in cases of acute or remote symptomatic seizures. Notably, the etiological landscape of seizures in children diverges significantly from that observed in adults, with neurodevelopmental, neurometabolic, and neuro-infectious factors emerging as predominant contributors. This multicentric study, conducted between November 2021 and November 2023, spanned diverse hospitals across various states in India. Encompassing children from birth to 12 years of age experiencing acute and remote symptomatic seizures, the study meticulously documented clinical and demographic profiles. Exclusion criteria were applied, excluding typical febrile seizures and idiopathic epilepsy syndromes to ensure a focused analysis. The study encompassed a total of 109 cases, revealing a spectrum of neuroimaging findings. Noteworthy among these were cortical malformations, including focal cortical dysplasia (12 cases), tuberous sclerosis (6 cases), polymicrogyria (3 cases), hemimegalencephaly (1 case), lissencephaly (1 case), schizencephaly (2 cases), heterotopias (3 cases), cavernous hemangioma (1 case), and AV malformation (1 case). Additionally, neoplastic lesions (6 cases), neurocysticercosis (5 cases), tuberculoma (4 cases), hippocampal sclerosis (3 cases), post-hypoxic and cerebrovascular accident gliosis (3 cases), leukodystrophies (2 cases), and non-lesional cases (58 cases) were documented. Pediatric neuroimaging in symptomatic seizures may present with normal findings, influenced by interpreter bias and the non-uniform availability of 3T MRI across different medical centers. The diverse causative factors for symptomatic seizures underscore the impact of demographic features, including the endemicity of specific infections and birth injuries, on the observed variability across medical centers. These findings underscore the imperative for a comprehensive understanding and standardization in pediatric neuroimaging practices.

Distinctive Craniofacial and Oral Anomalies in MN1 C-terminal Truncation Syndrome.

MN1 C-terminal truncation (MCTT) syndrome was first reported in 2020 and only 28 patients have been recorded to date. Since MCTT syndrome is a newly defined and rare syndrome with many clinical features, the present study reviewed the manifestations and management of oral and dental anomalies. Gene variants of MCTT syndrome and their positive phenotypes were summarised. The phenotypes of variants in two exons differed from each other mainly in the craniomaxillofacial region, including brain MRI abnormalities and palatal morphology. Pathogenic mechanisms, especially in craniofacial and oral anomalies, were discussed. Appropriate treatments in the stomatology and respiratory departments could improve the symptoms of MCTT syndrome. The different sites of MN1 gene variants may influence the clinical symptoms and there may be racial differences in MCTT syndrome. We recommend oral and pulmonary evaluations for the multidisciplinary treatment of MCTT syndrome.