Brain Magnetic Resonance Imaging Research Articles

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Circulating sphingolipids and subclinical brain pathology: the cardiovascular health study

BackgroundSphingolipids are implicated in neurodegeneration and neuroinflammation. We assessed the potential role of circulating ceramides and sphingomyelins in subclinical brain pathology by investigating their association with brain magnetic resonance imaging (MRI) measures and circulating biomarkers of brain injury, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in the Cardiovascular Health Study (CHS), a large and intensively phenotyped cohort of older adults.MethodsBrain MRI was offered twice to CHS participants with a mean of 5 years between scans, and results were available from both time points in 2,116 participants (mean age 76 years; 40% male; and 25% APOE ε4 allele carriers). We measured 8 ceramide and sphingomyelin species in plasma samples and examined the associations with several MRI, including worsening grades of white matter hyperintensities and ventricular size, number of brain infarcts, and measures of brain atrophy in a subset with quantitative measures. We also investigated the sphingolipid associations with serum NfL and GFAP.ResultsIn the fully adjusted model, higher plasma levels of ceramides and sphingomyelins with a long (16-carbon) saturated fatty acid were associated with higher blood levels of NfL [β = 0.05, false-discovery rate corrected P (PFDR) = 0.004 and β = 0.06, PFDR = < 0.001, respectively]. In contrast, sphingomyelins with very long (20- and 22-carbon) saturated fatty acids tended to have an inverse association with levels of circulating NfL. In secondary analyses, we found an interaction between ceramide d18:1/20:0 and sex (P for interaction = <0.001), such that ceramide d18:1/20:0 associated with higher odds for infarcts in women [OR = 1.26 (95%CI: 1.07, 1.49), PFDR = 0.03]. We did not observe any associations with GFAP blood levels, white matter grade, ventricular grade, mean bilateral hippocampal volume, or total brain volume.ConclusionOverall, our comprehensive investigation supports the evidence that ceramides and sphingomyelins are associated with increased aging brain pathology and that the direction of association depends on the fatty acid attached to the sphingosine backbone.

Narrow interval dual phase 18F-FDG PET/CT: A practical approach for distinguishing tumor recurrence from radiation necrosis in brain metastasis.

Purpose of our research is to demonstrate efficacy of narrow interval dual phase [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) imaging in distinguishing tumor recurrence (TR) from radiation necrosis (RN) in patients treated for brain metastases. 35 consecutive patients (22 female, 13 male) with various cancer subtypes, lesion size > 1.0 cm3, and suspected recurrence on brain magnetic resonance imaging (MRI) underwent narrow interval dual phase FDG-PET/CT (30 and 90 min after tracer injection). Clinical outcome was determined via sequential MRIs or pathology reports. Maximum standard uptake value (SUVmax) of lesion (L), gray matter (GM), and white matter (WM) was measured on early (1) and delayed (2) imaging. Analyzed variables include % change, late phase, and early phase for L uptake, L/GM uptake, and L/WM uptake. Statistical analysis (P < .01), receiver operator characteristic (ROC) curve and area under curve (AUC) cutoff values were obtained. Change in L/GM ratio of > -2% was 95% sensitive, 91% specific, and 93% accurate (P < .001, AUC = 0.99) in distinguishing TR from RN. Change in SUVmax of lesion alone was the second-best indicator (P < .001, AUC = 0.94) with an ROC cutoff > 30.5% yielding 86% sensitivity, 83% specificity, and 84% accuracy. Other variables (L alone or L/GM ratios in early or late phase, all L/WM ratios) were significantly less accurate. Utilizing narrow interval dual phase FDG-PET/CT in patients with brain metastasis treated with radiation therapy provides a practical approach to distinguish TR from RN. Narrow time interval allows for better patient comfort, greater efficiency of PET/CT scanner, and lower disruption of workflow.

A Study of Outcome of Detethering in Delayed Presentation of Tethered Cord Syndrome

Abstract Introduction Tethered cord syndrome (TCS) is a stretch-induced functional disorder associated with tethering of caudal spinal cord to dura mater with inelastic tissue, limiting its movement. It not only manifests in childhood but also noted in adults. Here, we studied the role of detethering on delayed presentation of TCS. Material and Methods All patients of symptomatic TCS from January 2011 to December 2019 were included. Preoperative evaluation was done through X-ray; ultrasonography kidney, ureter, urinary bladder; magnetic resonance imaging spine and brain; and urodynamic studies. Detethering was done and associated pathology was excised. Preoperative and postoperative comparison was done through Necker functional score using chi-squared system. Results Age range was 8 to 30 years with mean age of 13.8 years. Most common presentation was urological manifestations in ∼61.1% of cases followed by pain in 41.6%, sensory deficits in 38.8%, asymmetric weakness in 30.5%, orthopaedic and trophic ulcers in 22.2% each, and bowel abnormalities in 13.8% of cases. Mean duration of symptoms was 5.52 years. Improvement in urological manifestations was in 71.4% patients, pain in 80%, sensory dysfunctions in 71.4%, bowel dysfunctions in 80%, motor weakness in 90.9%, and trophic ulcer in 100% patients. On comparison, chi-squared value was 25.9993 and p-value was 0.000032, which was significant (&lt;0.05). Conclusion Detethering in early ages is already proven with good results. In our study, detethering showed statistically significant improvement even in delayed presentation. So, authors recommend to surgically interfere by detethering, even if tethering is detected late with significant deficits.

Brain MR image segmentation for tumor detection based on Riesz probability distributions

ABSTRACT This research introduces a new approach using the Riesz mixture model for medical image segmentation, specifically for diagnosing and treating brain tumors. We developed a novel technique for pixel classification based on the Riesz distribution, which is generated using an extended Bartlett decomposition. Our work is pioneering, as there are no existing studies addressing this issue in the literature. We aim to demonstrate the effectiveness of this distribution for brain image segmentation. We used the Expectation-Maximization algorithm to estimate the mixture parameters. To validate our segmentation algorithm, we conducted a comparative study with a recent method based on the Wishart distribution using Matlab software. Experiments with the Riesz mixture model showed that our method produces more intuitive results with a recognition rate of 94.52%. These results confirm the reliability of our method in detecting tumors using both synthetic and real brain images.

Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study

BackgroundLimited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet.MethodsParticipants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman’s rank-order correlation coefficient.ResultsThirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$($$\\end{document}± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant.ConclusionsAcquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample.Trial registrationThe study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538).

Abstract PO4-20-11: Excellent Response to Trastuzumab Deruxtecan in “HER-2 Low” Leptomeningeal Breast Cancer and Diagnostic and Monitoring Utility of CNSide Assay

Abstract A 72-year-old female presented to the emergency department with sudden onset of imbalance and cognitive decline. Two years ago, she was diagnosed with stage III estrogen receptor (ER) positive, progesterone receptor (PR) negative and human epidermal growth factor receptor 2 (HER2) negative (immunohistochemistry score: 0) invasive breast cancer. She subsequently underwent lumpectomy, adjuvant radiation and chemotherapy. She had been taking adjuvant letrozole at the time of presentation. Magnetic resonance imaging (MRI) of the brain showed obstructive hydrocephalus without any parenchymal lesion or leptomeningeal enhancement. MRI of spine, computerized tomography of chest/abdomen/pelvis and bone scan were unrevealing except a left adrenal mass which was equivocal for metastasis. Cerebrospinal fluid (CSF) analysis showed rare malignant cells. Based on the morphology of malignant cells and the patient’s prior history of breast cancer, the diagnosis of leptomeningeal disease with breast primary was made. There were insufficient cells for additional studies. CNSide, a microfluidic platform that uses antibody capture method to detect CSF tumor cells, showed 4678 tumor cells per milliliter (ml) of CSF. Immunocytochemistry of these cells showed that the cells were ER negative (criterion: &amp;lt; 2.6% ER positive cells) and programmed cell death ligand 1 (PD-L1) negative (criterion: &amp;lt; 3.4% PD-L1 positive cells). Fluorescence in situ hybridization (FISH) testing showed 22% of tumor cells with HER2 amplification (either HER2 to CEP17 ratio &amp;gt;/= 2.0 or HER2 copy number &amp;gt;/= 6). She underwent ventriculoperitoneal (VP) shunt placement with complete recovery of her neurological symptoms. Five weeks later, she was started on trastuzumab deruxtecan (T-DXd) mainly because of the HER2 FISH results from CNSide. She did not receive central nervous system(CNS) irradiation due to complete resolution of neurological symptoms with VP shunt placement. After two cycles of T-DXd, MRI brain showed mild leptomeningeal enhancement along bilateral superior cerebellar sulci. Positron emission tomography/computed tomography(PET/CT) scan showed a new sclerotic rib focus, thought to represent treatment response. It also showed decrease in size of the adrenal mass. The adrenal mass was hypermetabolic on PET, consistent with malignancy. CSF cytology was negative, while CSF tumor cell count significantly reduced to 6.72 cells/ml using CNSide. There were no new neurological symptoms. Despite MRI brain findings, she was continued on T-DXd given overall favorable clinical, radiological and CSF data. After 7th cycle of T-DXd, repeat CSF cytology continued to remain negative, CNSide CSF tumor cell count decreased further to 6.27 cells/ml, MRI brain showed interval resolution of prior leptomeningeal enhancement, CT scan showed further decrease in size of the adrenal mass (baseline 5.4cm to 3cm), stable rib sclerotic lesion and no additional metastatic lesion. To date, she has undergone eight cycles of T-DXd which has effectively controlled her disease for a period of seven months. She has not needed VP shunt revision and continues to do well. To our knowledge, this is the first case demonstrating the activity of T-DXd against CSF tumor cells where a small subset exhibited HER2 amplification. This case also highlights the benefits of using advanced and highly sensitive CSF assays to diagnose and surveil leptomeningeal disease and to assess HER2 signaling. This case is well-timed especially in the era of next-generation, CNS penetrating, potent HER2-directed antibody-drug conjugate like T-DXd which can mount antitumor activity even with minimal HER2 expression in tumors. Citation Format: Sarah Stanford, Zeni Kharel, Lauryn Hemminger, Tyler Schmidt, Sara Hardy, Jason Zittel, Nimish Mohile, Ajay Dhakal. Excellent Response to Trastuzumab Deruxtecan in “HER-2 Low” Leptomeningeal Breast Cancer and Diagnostic and Monitoring Utility of CNSide Assay [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-20-11.

Brainstem Chipmunk Sign: A Diagnostic Imaging Clue across All Subtypes of Alexander Disease.

While classic brain MR imaging features of Alexander disease have been well-documented, lesional patterns can overlap with other leukodystrophies, especially in the early stages of the disease or in milder phenotypes. We aimed to assess the utility of a new neuroimaging sign to help increase the diagnostic specificity of Alexander disease. A peculiar bilateral symmetric hyperintense signal on T2-weighted images affecting the medulla oblongata was identified in an index patient with type I Alexander disease. Subsequently, 5 observers performed a systematic MR imaging review for this pattern by examining 55 subjects with Alexander disease and 74 subjects with other leukodystrophies. Interobserver agreement was assessed by the κ index. Sensitivity, specificity, and receiver operating characteristic curves were determined. The identified pattern was present in 87% of subjects with Alexander disease and 14% of those without Alexander disease leukodystrophy (P < .001), 3 with vanishing white matter, 4 with adult polyglucosan body disease, and 3 others. It was found equally in both type I and type II Alexander disease (28/32, 88% versus 18/21, 86%; P = .851) and in subjects with unusual disease features (2/2). Sensitivity (87.3%; 95% CI, 76.0%-93.7%), specificity (86.5%; 95% CI, 76.9%-92.5%), and interobserver agreement (κ index = 0.82) were high. The identified pattern in the medulla oblongata, called the chipmunk sign due to its resemblance to the face of this rodent, is extremely common in subjects with Alexander disease and represents a diagnostic tool that can aid in early diagnosis, especially in subjects with otherwise atypical MR imaging findings and/or clinical features.

Hemorrhagic Transformation in Noncardioembolic Acute Ischemic Stroke: MRI Analysis From PACIFIC-STROKE.

In the phase 2 PACIFIC-STROKE trial (Proper Dosing and Safety of the Oral FXIa Inhibitor BAY 2433334 in Patients Following Acute Noncardioembolic Stroke), asundexian, an oral factor XIa inhibitor, did not increase the risk of hemorrhagic transformation (HT). In this secondary analysis, we aimed to investigate the frequency, types, and risk factors of HT on brain magnetic resonance imaging (MRI). This was a secondary analysis of the PACIFIC-STROKE trial. Patients with mild-to-moderate acute noncardioembolic ischemic stroke were randomly assigned to asundexian or placebo plus guideline-based antiplatelet therapy. Brain MRIs were required at baseline (≤120 hours after stroke onset) and at 26 weeks or end-of-study. HT was defined using the Heidelberg classification and classified as early HT (identified on baseline MRI) or late HT (new HT by 26 weeks) based on iron-sensitive sequences. Multivariable logistic regression models were used to test factors that are associated with early HT and late HT, respectively. Of 1745 patients with adequate baseline brain MRI (mean age, 67 years; mean National Institutes of Health Stroke Scale score, 2.8), early HT at baseline was detected in 497 (28.4%). Most were hemorrhagic infarctions (hemorrhagic infarction type 1: 15.2%; HI2: 12.7%) while a few were parenchymal hematomas (parenchymal hematoma type 1: 0.4%; parenchymal hematoma type 2: 0.2%). Early HT was more frequent with longer symptom onset-to-MRI interval. Male sex, diabetes, higher National Institutes of Health Stroke Scale large (>15 mm) infarct size, cortical involvement by infarct, higher number of acute infarcts, presence of chronic brain infarct, cerebral microbleed, and chronic cortical superficial siderosis were independently associated with early HT in the multivariable logistic regression model. Of 1507 with follow-up MRI, HT was seen in 642 (42.6%) overall, including 361 patients (23.9%) with late HT (new HT: 306; increased grade of baseline HT: 55). Higher National Institutes of Health Stroke Scale, large infarct size, cortical involvement of infarct, and higher number of acute infarcts predicted late HT. About 28% of patients with noncardioembolic stroke had early HT, and 24% had late HT detectable by MRI. Given the high frequency of HT on MRI, more research is needed on how it influences treatment decisions and outcomes.

Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection

Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task. We compare the performance of three State-of-the-Art AD algorithms DeepSVDD, Interpolated Gaussian Descriptor and FewSOME on two open-source Brain MRI datasets on the task of MAD and MA severity classification, with FewSOME achieving a MAD AUC >90% on both datasets and a Spearman Rank Correlation Coefficient of 0.8 on the task of MA severity classification. These models are trained in the few shot setting, meaning large Brain MRI datasets are not required to build robust MAD algorithms. This work also sets a standard protocol for testing MAD algorithms on open-source benchmark datasets. In addition to addressing these challenges, we demonstrate how our proposed ‘anomaly-aware’ scoring function improves FewSOME’s MAD performance in the setting where one and two shots of the anomalous class are available for training. Code available at https://github.com/niamhbelton/Unsupervised-Brain-MRI-Motion-Artefact-Detection/.

Neuro-Behcet’s Disease Manifesting as Bilateral Fornix and Left Thalamus Infarction

Neuro-Behcet’s disease (NBD) is defined as neurological involvement in Behcet’s disease. A 32-year-old man was admitted due to sudden memory impairment and had a history of recurrent oral/genital ulcers. Brain magnetic resonance imaging (MRI) revealed ischemic lesions in the bilateral fornix and left thalamus, and high-resolution vessel wall MRI demonstrated vasculitis in the anterior communicating artery. We report a rare case of NBD that presented as vasculitis and ischemic stroke in the bilateral fornix and thalamus.

Association between Early Basal Ganglia and Thalami Perfusion Assessed by Color Doppler Ultrasonography and Brain Injury in Infants with Hypoxic-Ischemic Encephalopathy: A Prospective Cohort Study.

To evaluate associations between neurologic outcomes and early measurements of basal ganglia (BG) and thalamic (Th) perfusion using color Doppler ultrasonography (CDUS) in infants with hypoxic-ischemic encephalopathy (HIE). Prospective study of infants with mild (n=18), moderate (n=17), and severe HIE (n=14) and controls (n=17). Infants with moderate-severe HIE received therapeutic hypothermia (TH). CDUS was performed at 24-36h and brain magnetic resonance imaging (MRI) at a median of 10 days. Development was followed through 2.5 to 5 years. The primary outcome was the association between BG and Th perfusion and brain MRI injury. Secondary analyses focused on associations between perfusion measurements and admission neurologic examinations, MRI scores in infants treated with TH, and motor and sensory disability, or death. An exploratory analysis assessed the accuracy of BG and Th perfusion to predict brain MRI injury in infants treated with TH. Increased BG and Th perfusion on CDUS was observed in infants with severe MRI scores and those with significant motor and neurosensory disability or death through 2.5 to 5 years (p<0.05). Infants with severe HIE showed increased BG and Th perfusion (p< .005) compared with infants with moderate HIE. No differences were identified between the between the control and mild HIE groups. Th perfusion ≥ 0.237 cm/sec (AUC of 0.824) correctly classified 80% of infants with severe MRI scores. Early dynamic CDUS of the BG and Th is a potential biomarker of severe brain injury in infants with HIE and may be a useful adjunct to currently used assessments.

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

High Physiological 18F-FDG Uptake in Normal Pituitary Gland on Digital PET Scanner

Association of Incident Stroke Risk With an IL-18-Centered Inflammatory Network Biomarker Composite.

A coordinated network of circulating inflammatory molecules centered on the pleotropic pro-atherogenic cytokine interleukin-18 (IL-18) is linked to cerebral small vessel disease. We sought to validate the association of this inflammatory biomarker network with incident stroke risk, cognitive impairment, and imaging metrics in a sample of the Framingham Offspring Cohort. Using available baseline measurements of serum levels of IL-18, GDF (growth and differentiation factor)-15, soluble form of receptor for advanced glycation end products, myeloperoxidase, and MCP-1 (monocyte chemoattractant protein-1) from Exam 7 of the Framingham Offspring Cohort (1998-2001), we constructed a population-normalized, equally weighted log-transformed mean Z-score value representing the average level of each serum analyte to create an inflammatory composite score (ICS5). Multivariable regression models were used to determine the association of ICS5 with incident stroke, brain magnetic resonance imaging features, and cognitive testing performance. We found a significant association between ICS5 score and increased risk for incident all-cause stroke (hazard ratio, 1.48 [95% CI, 1.05-2.08]; P=0.024) and ischemic stroke (hazard ratio, 1.51 [95% CI, 1.03-2.21]; P=0.033) in the Exam 7 cohort of 2201 subjects (mean age 62±9 years; 54% female) aged 45+ years with an all-cause incident stroke rate of 6.1% (135/2201) and ischemic stroke rate of 4.9% (108/2201). ICS5 and its component serum markers are all associated with the Framingham Stroke Risk Profile score (β±SE, 0.19±0.02; P<0.0001). In addition, we found a significant inverse association of ICS5 with a global cognitive score, derived from a principal components analysis of the neuropsychological battery used in the Framingham cohort (-0.08±0.03; P=0.019). No association of ICS5 with magnetic resonance imaging metrics of cerebral small vessel disease was observed. Circulating serum levels of inflammatory biomarkers centered on IL-18 are associated with an increased risk of stroke and cognitive impairment in the Framingham Offspring Cohort. Linking specific inflammatory pathways to cerebral small vessel disease may enhance individualized quantitative risk assessment for future stroke and vascular cognitive impairment.

West Syndrome due to ALG13 mutation presenting vigabatrin-associated reversible MRI signal changes and drug-induced dyskinesia

Heterozygous mutations in the asparagine-linked glycosylation 13 (ALG13) gene cause a rare genetic disorder that severely affects neurological development. Symptoms associated with ALG13 mutations include severe epilepsy and/or West syndrome (WS). Most cases reported in the literature describe typical characteristics of patients within whom de novo mutations have been identified. A 5-month-old girl with developmental delay and epileptic spasms, without dysmorphic features, is described. Electroencephalography (EEG) showed hypsarrhythmia, compatible with WS, but complementary examinations did not find an underlying etiology. Vigabatrin therapy was started, and then adrenocorticotropic hormone (ACTH) therapy was added. On follow-up, brain magnetic resonance imaging (MRI) revealed restricted diffusions compatible with vigabatrin-associated reversible MRI signal changes. As the spasms and hypsarrhythmia on EEG persisted, a second cycle of ACTH was performed, and then, the patient manifested a hyperkinetic movement, characteristic of drug-induced dyskinesia. Further investigation was carried out, and whole-exome sequencing revealed a de novo pathogenic variant in ALG13 gene. The current case report expands our knowledge of WS by considering a female patient with unspecific clinical features arising from an undefined subjacent etiology. This case also emphasizes the importance of a thorough work-up to determine the underlying genetic etiology and presents the challenges in the diagnostic investigation in clinical practice.

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

     An accumulation of aberrant cells called a brain tumor is the outcome of unregulated cell division. Brain cancers can be found using magnetic resonance imaging (MRI). The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and have high performance. This dearth of training data makes it critical to explore methods such as data augmentation. In this sense, data augmentation methods are widely used in strategies to train networks, and with small data sets being vital in medicine due to the limited access to data, this work aims to identify the best classification system by considering the prediction accuracy in this vein. Data augmentation is performed on the database and fed into the three convolutional neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance on the Inception v3 models, Mobile Net V2, and Squeeze Net network for brain tumor detection and classifying 350 brain MR images. The statistical methods were modified in order to evaluate these algorithms. With 0.992% accuracy, 0.993% recall, 0.989% precision, and 0.994% F1 score, the Squeeze Net model performed the best. The Mobile Net V2 model, which had an accuracy of 0.964%, came next. When the research's findings were compared to those of related studies in the literature, they revealed better success rates than those of the majority of investigations.

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

Autoimmune Encephalitis following Checkpoint Inhibitor Therapy in a Patient with Metastatic Melanoma in Complete Remission

The use of immune checkpoint inhibitors (ICIs) in cancer is increasing. Their side effects are mainly due to the triggering of autoimmunity, which are mild or moderate and include skin rash, colitis, hepatitis, endocrine disorders, myositis, interstitial lung disorder, etc., in most cases during the course of therapy. Autoimmune encephalitis (AE) is rare in cancer patients treated with ICIs. Fifty patients with ICI-related encephalitis were identified in a recent review. Herein, we report a case of pembrolizumab associated with AE with a favorable short-term prognosis. A 68-year-old man with malignant metastatic melanoma achieved complete remission after pembrolizumab treatment. However, 10 months after pembrolizumab cessation due to grade 3 diarrhea, he developed confusion, an altered mental status, progressive memory loss, and gait disturbance. He was admitted to the neurologic department, and a comprehensive neurological workup, brain magnetic resonance imaging, cerebral fluid analysis, EEG, and blood test allowed the diagnosis of autoimmune encephalitis. The patient was treated with plasmapheresis, a high dose of intravenous steroids, and intravenous immunoglobulins. The patient improved, and he is now well with a performance status of 1. This case is interesting since the AE developed approximately 10 months after the cessation of immunotherapy, the underlying cancer was in complete remission, and the AE showed a good response after the treatment was performed.

Bilateral hearing loss as the initial presentation of reversible Wernicke's encephalopathy with splenial lesion.

Wernicke's encephalopathy (WE) is an acute neurological syndrome resulting from thiamine (vitamin B1) deficiency. It has been recognized increasingly in non-alcoholic patients, such as in the condition of malnutrition. Recent literature has shed light on uncommon symptoms and neuroimaging findings. We reported a case of a 44-year-old male who initially presented with bilateral hearing loss, and exhibited abnormality in the splenium of the corpus callosum on magnetic resonance imaging(MRI) diffusion-weighted imaging sequence. On the following day the patient developed new symptoms, including unstable walking, double vision and hallucination. The subsequent brain MRI demonstrated lesions involving periaqueductal grey matter and bilateral medial thalamus, indicating the diagnosis of WE. Empirical treatment with intravenous thiamine resulted in complete clinical and radiological resolution. To the best of our knowledge, the current case is the first report of WE in literature with uncommon but reversible manifestations. This case warns us to maintain a heightened level of suspicion for WE in malnourished patients with neurological deficits, despite the possibility of atypical presentations encompassing bilateral hearing disturbances and unusual neuroradiological results. Early diagnosis and timely administration of thiamine in WE are likely to lead to a favorable outcome and full recovery.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3+ (MSGAM-AUNet3+), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.