Meningioma Tumor Research Articles

DISP-20. SEX DIFFERENCES IN MENINGIOMA AGGRESSIVE BEHAVIOR IN RURAL POPULATIONS: A GEO-ECOLOGICAL RISK ASSESSMENT OF MENINGIOMAS IN A SINGLE RURAL HEALTH CARE SYSTEM

Abstract BACKGROUND Cause for health outcome disparities in rural populations is multifactorial. These populations are potentially more vulnerable due to rural area environmental policies and practices. Environmental justice populations (EJP) are designated by the Environmental Protection Agency as at risk for generational exposer to environmental contaminants. We assessed these population for an association between EJP density and aggressiveness of indolent brain tumors. Meningiomas are the most common benign intracranial tumor and have a female predominance. We evaluated meningioma patients in a large rural health system for oncogenic risk associated geo-ecological factors. METHODS A retrospective review of 415 pathology confirmed CNS WHO grades 1-3 meningiomas cases between 2007 – 2017 within the Geisinger Health system in Pennsylvania. Cases were mapped by county for meningioma incidence, tumor grade, and tumor recurrence. Counties were rated for EJP density. We mapped geographic areas with a higher meningioma incidence, grade, and rates of recurrence, and evaluated sex-predilections. RESULTS Seventy-three percent of tumors were WHO grade 1 (n=303), 23.4% (n=97) were grade 2, and 3.6% (n=15) were grade 3. The highest incidence of meningiomas were localized to higher density EJP counties that bordered riverbeds, Lackawanna and Luzerne. Females had a 3.5:1 predominance as compared to males. The grade 1 10-year progression free survival rate was 54.7% in Luzerne, Columbia, and Montour counties (moderate to high density EJP). Males in high EJP counties had a 12% increased risk for developing high grade meningiomas. Males aged 51-60 were more likely to develop a grade 2 or 3 meningioma as compared to males aged 61-70 in these counties. Females aged 50-60 more likely had grade 1 meningiomas but demonstrated consistently increasing risk for recurrence every decile. CONCLUSIONS Pennsylvania high density EJP counties (especially along the Lackawanna River supply) have a disproportionate risk for aggressive meningiomas and lower progression free survival as compared to national averages. A sex-discrepancy exists for males in these areas to have the most aggressive tumors.

TMET-38. BIOCHEMICAL CHARACTERIZATION OF PSAMMOMATOUS MENINGIOMA USING MAGNETIC RESONANCE SPECTROSCOPY

Abstract Psammomatous meningioma is a histologic subtype of meningioma and is presented as a highly calcified intracranial or spinal lesion. Although computed tomography (CT) and short/zero echo-time MRI techniques can detect calcification in tumors, a complete biochemical profile of psammomatous meningiomas is not known. Here, we compare the metabolomic profiles of common meningiomas (WHO grade-I and grade-II) and a psammomatous meningioma using ex vivo proton magnetic resonance spectroscopy (1H MRS). The tumor specimens were collected from meningioma patients undergoing surgery for the resection of the tumor mass. Tumor samples were extracted in 5% perchloric acid, centrifuged, supernatants were vacuum dried and reconstituted in D2O containing 1.0 mM sodium 2,2-dimethyl- 2-silapentane-5-sulfonate-d₆ (DSS-d6, internal standard). Previously, we have identified the following metabolites in grade-I and grade-II meningioma tumors: leucine, isoleucine, valine, lactate, alanine, acetate, glutamate, succinate, glutamine, aspartate, creatine, phosphocreatine, phosphocholine, glycerophosphocholine, myo-inositol, scyllo-inositol, taurine, hypotaurine, glycine, phosphoethanolamine, and glucose. In the current study, psammomatous meningioma also showed the presence of all the above metabolites; however, the levels of these metabolites were lower concentrations compared to the grade-I and grade-II meningiomas, except for lactate. In addition to the above-mentioned metabolites, psammomatous meningioma showed the presence of citrate which was not detected in other meningioma subtypes. Citrate is a major component of bone and the presence of citrate (~614 µM/g, tissue) in psammomatous meningioma could be due to the calcification occurring in these tumors. Surgical pathology report of resected tissue specimen showed clusters and small whorls of meningothelial cells with scattered calcifications, consistent with psammomatous meningioma (WHO grade-I; Ki-67 = 3%). In conclusion, detection of citrate by 1H MRS in meningiomas can be helpful in the differential diagnosis of psammomatous meningiomas.

IMMU-54. THERAPEUTIC TARGETING OF STING IN MENINGIOMAS

Abstract Up to one-quarter of meningiomas recur within five years of resection; however, there are currently no established adjuvant therapies for lesions that are intractable to surgery and radiation. To identify novel treatment approaches, scRNA-sequencing datasets were reanalyzed from thirteen meningiomas and nine adjacent dura samples including >30,000 immune cells from varying WHO grades and molecular subtypes. In contrast to gliomas, immune-inducing STING (stimulator of interferon genes) was expressed within the meningioma tumor cells, consistent with DNA methylation profiling that showed hypomethylation of the STING promotor in 93% of cases (267 of 288 meningiomas; beta < 0.2). Meningiomas in the immune-enriched subgroup additionally exhibited significant hypomethylation in the 3’ UTR of this gene (p = 1.9 x 10-9). Using sequential multiplex immune fluorescence, STING expression was confirmed in neoplastic, myeloid, and endothelial cells within all grades of meningiomas, but not in adjacent dura and brain. Downstream STING pathway activation markers such as p-IRF3 were partially expressed, suggesting that further activation of the STING pathway may offer therapeutic benefits. Ex vivo single-cell suspensions of resected human meningiomas treated with the STING agonist 8803 induced 25-45% growth inhibition in 7/8 cases within 72 hours (p < 0.001; two-way ANOVA). There was no growth inhibition of the meningioma cell lines IOMM-LEE and CH157 treated with 8803, which lack immune cells and contain only clonal neoplastic cells. However, when the IOMM-LEE meningioma cells were implanted in vivo, a single dose of 8803 induced a >50% reduction in tumor volume relative to the vehicle control injection at multiple time points (p < 0.001; two-way ANOVA) indicating that the immune system is an essential component of STING efficacy in these tumors. Our results reveal widespread expression of STING in neoplastic and myeloid populations in meningiomas and clarify a new treatment indication for use of the STING agonist 8803.

Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis

The classification of brain tumors from medical imaging is pivotal for accurate medical diagnosis but remains challenging due to the intricate morphologies of tumors and the precision required. Existing methodologies, including manual MRI evaluations and computer-assisted systems, primarily utilize conventional machine learning and pre-trained deep learning models. These systems often suffer from overfitting due to modest medical imaging datasets and exhibit limited generalizability on unseen data, alongside substantial computational demands that hinder real-time application. To enhance diagnostic accuracy and reliability, this research introduces an advanced model utilizing the Xception architecture, enriched with additional batch normalization and dropout layers to mitigate overfitting. This model is further refined by leveraging large-scale data through transfer learning and employing a customized dense layer setup tailored to effectively distinguish between meningioma, glioma, and pituitary tumor categories. This hybrid method not only capitalizes on the strengths of pre-trained network features but also adapts specific training to a targeted dataset, thereby improving the generalization capacity of the model across different imaging conditions. Demonstrating an important improvement in diagnostic performance, the proposed model achieves a classification accuracy of 98.039% on the test dataset, with precision and recall rates above 96% for all categories. These results underscore the possibility of the model as a reliable diagnostic tool in clinical settings, significantly surpassing existing diagnostic protocols for brain tumors.

Efficient brain tumor grade classification using ensemble deep learning models

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

IMPACT OF DNA METHYLATION PROfiLING IN REfiNING MENINGIOMA RECURRENCE RISK STRATIfiCATION AND CLINICAL FOLLOW UP

Abstract AIMS To assess clinical impact of the additive information gained from Integrated Meningioma Risk Score on the risk of meningioma recurrence. METHOD DNA methylation (Illumina EPIC Array) analysis was performed on all recurrent meningioma tumours, primary tumours CNS5 WHO grade 2, 3 and grade 1 with unusual morphological features from Dec 2021 to Jan 2024. DNA methylation analysis was performed, and data files were processed on the DKFZ Heidelberg Classifier v12.5/6 (molecularneuropathology.org). The output from the classifier including methylation class combined with WHO Grade and chromosomal loss (1p, 6q,14q) was used to produce an integrated meningioma risk score for recurrence. RESULTS 70 meningioma cases were profiled. This included 68 cranial meningiomas, ((WHO grade 1, n=19; recurrences=7), (grade 2, n=47; recurrences =9), (grade 3 n=2; recurrences=1)) and 2 spinal meningiomas (grade 1 n=1, grade2 n=1). 3 tumours were excluded due to poor calibration scores. Of the 67 cases the diagnosis was revised in 1 case following DNA methylation analysis due to presence of CDKN2A/B loss. Molecular-morphologic integrated risk score was calculated for all 67 cases. In 10% of overall patients the predicted risk of meningioma recurrence was increased to either intermediate or high, whilst 32% had the risk lowered mainly from intermediate to low. Interestingly, 1 patient who underwent a gross total resection of a WHO Grade 2 meningioma, experienced recurrence with the integrated meningioma risk score indicating low risk of recurrence, which is contrary to the clinical aggressiveness of the tumour. CONCLUSION Meningioma integrated risk stratification by DNA methylation profiling has shown concordance of integrated risk score with WHO Grade in 58% of cases. In 42% of cases the predicted risk was altered when compared to the WHO Grade alone. These results need to be further validated with long term data on patient outcomes.

Apraxia and Malformation Severity in Brain Tumor Disease: Case Report and Literature Review

Objective: In apraxia, the lack of skill in doing a previously learned task occurs without obvious weakness in the limbs. Material and Method: In this case report, we present a with brain atrophy, diagnosed with posterior cortical atrophy and meningioma tumor in the frontal region, has referred to the clinic. In this patient, due to the analysis of the parietal and frontal cortex of the patient's brain, despite being able to write and draw shapes in the past, they now forget this skill and suffer from ideomotor apraxia. Also, this patient has slowness and weakness due to severe tumor involvement. Result: Accurate and timely diagnosis of especially in prevalent can prevent the unwarranted complications and reduce the morbidity and mortality associated with in allogeneic/autologous transplant recipients. Conclusion: This particular disorder is seen in patients with degeneration of the parietal and frontal cortex of the brain (in dementia, brain tumors and stroke).

Grade-stratified meningioma risk among individuals who are Non-Hispanic black and interactions with male sex.

Meningioma risk factors include older age, female sex, and African-American race. Limited data explore how meningioma risk in African-Americans varies across the lifespan, interacts with sex, and differs by tumor grade. The Central Brain Tumor Registry of the United States (CBTRUS) is a population-based registry covering the entire U.S. population. Meningioma diagnoses from 2004-2019 were used to calculate incidence rate ratios (IRRs) for non-Hispanic Black individuals (NHB) compared to non-Hispanic white individuals (NHW) across 10-year age intervals, and stratified by sex and by WHO tumor grade in this retrospective study. 53,890 NHB individuals and 322,373 NHW individuals with an intracranial meningioma diagnosis were included in analyses. Beginning in young adulthood, the NHB-to-NHW IRR was elevated for both grade 1 and grade 2/3 tumors. The IRR peaked in the seventh decade of life regardless of grade, and was higher for grade 2/3 tumors (IRR = 1.57; 95% CI: 1.46-1.69) than grade 1 tumors (IRR = 1.27; 95% CI: 1.25-1.30) in this age group. The NHB-to-NHW IRR was elevated in females (IRR = 1.17; 95% CI: 1.16-1.18) and was further elevated in males (IRR = 1.28; 95% CI: 1.26-1.30), revealing synergistic interaction between NHB race/ethnicity and male sex (PInteraction=0.001). Relative to NHW individuals, NHB individuals are at elevated risk of meningioma from young adulthood through old age. NHB race/ethnicity conferred greater risk of meningioma among men than women, and greater risk of grade 2/3 tumors. Population-level differences in meningioma incidence and tumor behavior suggest potential disparities in the geographic, socioeconomic, and racial distribution of meningioma risk factors within the U.S.

Atlas-Based Structural Disconnectomes Are Associated with Cognitive Performance in Brain Tumors.

Background: Brain tumors are associated with impaired cognitive functioning, which may result from disruptions in brain structural connectivity. Estimating structural disconnections is a more advantageous representation of tumor impact and can be performed indirectly through normative brain atlases. Materials and Methods: Using a publicly available dataset of glioma and meningioma patient MRI scans and tumor masks, latent correlations were estimated between measures of structural disconnection and attention-based cognitive functioning. These measures included gray matter (GM) parcel damage, white matter tract damage, GM parcel-to-parcel disconnections, and reaction time (RTI) as part of the Cambridge Neuropsychological Test Automated Battery to assess attention. Results: Preprocessing pipelines with two different methods of minimizing the pathology impact on MRI normalization were utilized: cost-function masking and lesion filling. The results across both pipelines were nearly consistent, with significant correlations mainly found between RTI measures and the damage to the left inferior fronto-occipital and uncinate fasciculus, as well as the left prefrontal-visual disconnections. Conclusions: This alludes to the importance of left-hemispheric prefrontal-visual coupling in attention-based tasks, particularly those involving object- and feature-based attention.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

Computationally optimized brain tumor classification using attention based GoogLeNet-style CNN

Early detection of diseases is a crucial step towards patient healthcare and recovery, especially, for low survival rate diseases like brain cancer. In recent years, deep learning models such as convolutional neural networks (CNNs) have been widely utilized for medical image classification owing to their proficiency in extracting local features. However, accurate classification of complex medical images necessitates capturing local and global features. This paper focuses on the classification of the three most prominent kinds of brain tumors, namely, glioma, meningioma and pituitary tumor, with the proposed CNN based on the structure of GoogLeNet. The proposed model uses an attention-based inception module (ABIM) embedded with attention mechanism of spatial attention and efficient channel attention. Attention helps CNN focus on features with more distinguishing power and ignores irrelevant features while the inception approach aids in extracting multiscale local features. ABIM incorporates attention with inception to generate better feature representation. The model training and testing are done on a publicly accessible Figshare dataset with 3064 brain MRI samples. The dataset comprises 1426, 708, and 930 MRI samples of glioma, meningioma, and pituitary tumors, respectively. In a five-fold cross-validation experiment, the proposed model achieved an average accuracy of 97.97% for glioma, 94.76% for meningioma, and 99.22% for pituitary tumors. The proposed CNN model surpasses existing models with an overall accuracy of 97.62%, while utilizing fewer parameters than its base model, GoogLeNet. The model is also evaluated on datasets from the Kaggle and Harvard medical repositories, and showed promising results in distinguishing between different tumor classes, and normal brain MRIs. These results demonstrate that our model not only outperforms other existing models in terms of accuracy but also has comparatively lower computational complexity, providing a robust tool to support healthcare professionals.

Improved Brain Tumor Detection MRI Using Advanced Processing Techniques: Enhancement and Convolution Case Studies

Brain tumors present a significant challenge in medical imaging due to their complexity, requiring early detection and precise analysis for effective treatment. This study develops and evaluates advanced image processing workflows aimed at enhancing brain tumor image analysis. The proposed method involves four main steps: enlargement, pre-processing with min-max filters, enhancement, and convolution. The dataset used is from Kaggle, comprising 3,364 images categorized into Glioma (100 images), Meningioma (115 images), No Tumor (105 images), and Pituitary Tumor (74 images). For this study, images from the Glioma, Meningioma, and Pituitary Tumor categories were used, with one image selected from each category for technique evaluation. The results showed significant improvements in image clarity and detail, with high correlation values of 0.9851 for Meningioma and 0.9886 for Pituitary. These findings highlight the effectiveness of the proposed techniques in enhancing image quality and diagnostic accuracy.

Multi Graded Brain Tumor Classification using YOLOv7

In recent years, significant progress has been made in the field of diagnosis and classification of brain tumors, mainly attributed to advancements in artificial intelligence and medical imaging. The main objective of this study is to improve the detection and classification of brain tumors by applying and utilizing of artificial intelligence (AI) and recent advancements in medical imaging techniques. Automation of the process of tumor identification and then classification, in addition to tumor grading, will definitely improve all procedures of brain tumor treatment and enhance patient care. The proposed system combines convolutional neural networks (CNNs), which act as extract features, and the You Only Look Once Algorithm (YOLOv7) for effective object identification and accurate classification. The methodology described in this study involves employing a technique of multilayer classification, which integrates three distinct datasets. This comprehensive approach shows an exceptional levels of accuracy and precision. At the initial level, the model attains a 99.78% accuracy in distinguishing between tumor and nontumor cases. At the next level the system accurately sorts types of brain tumors (such, as glioma, meningioma and pituitary tumors) with an average accuracy of 99.35%. Moving on to the final stage it successfully distinguishes between low grade and high grade glioma tumors with a precision of 93.07%. Moreover the model shows accuracies ranging from 99.41%, to 99.61% when classifying types of brain tumors and nontumor cases. The proposed system has the ability to determine the boundaries of the tumor, and thus this has helped in calculating the sizes of tumors with high accuracy. Index Terms- Brain tumor, MRI, Convolutional Neural Network, YOLO, Tumor grading.

A phase II, open-label, single-arm trial of pembrolizumab for recurrent meningioma and solitary fibrous tumor.

Atypical and anaplastic meningiomas account for 20% of all meningioma cases. Solitary fibrous tumor (SFT) is a type of soft tissue sarcoma with similar attributes to meningioma. For patients with refractory or recurrent disease after previous surgery or radiotherapy, there is no effective treatment. Pembrolizumab, an anti-programmed cell death 1 (PD-1) antibody, is an effective treatment for various solid tumors. PD-1 ligand is highly expressed in aggressive meningiomas. We aimed to assess the effectiveness of pembrolizumab in treating meningioma and SFT recurrence after surgery and radiation therapy. This prospective single-arm phase II trial comprised 15 patients with recurrent meningioma and 3 with anaplastic SFT, treated at a single institution during 2018 to 2022. The study was terminated due to a lack of efficacy and slow accrual. The primary endpoint was 6-month progression-free survival (PFS-6). Median progression-free survival (PFS) was 2.6 months, and median overall survival (OS) was 40 months. The 6- and 12-month PFS were both 11.1%. The 6- and 12-month OS were 94.4% and 61.1%, respectively. According to the Response Assessment in Neuro-Oncology (RANO) criteria, the overall response rate was 11%, with 2 patients achieving stable disease and 2 with partial response. Three patients (16.7%) developed grade 3 toxicity. Our results showed that pembrolizumab failed to improve PFS-6 in patients with aggressive meningioma or anaplastic SFT. However, two patients, one with atypical meningioma and one with anaplastic SFT, achieved a partial response. More clinical studies are needed to identify which subset of patients may benefit from this treatment.

A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%.

MerlinS13 phosphorylation regulates meningioma Wnt signaling and magnetic resonance imaging features

Meningiomas are associated with inactivation of NF2/Merlin, but approximately one-third of meningiomas with favorable clinical outcomes retain Merlin expression. Biochemical mechanisms underlying Merlin-intact meningioma growth are incompletely understood, and non-invasive biomarkers that may be used to guide treatment de-escalation or imaging surveillance are lacking. Here, we use single-cell RNA sequencing, proximity-labeling proteomic mass spectrometry, mechanistic and functional approaches, and magnetic resonance imaging (MRI) across meningioma xenografts and patients to define biochemical mechanisms and an imaging biomarker that underlie Merlin-intact meningiomas. We find Merlin serine 13 (S13) dephosphorylation drives meningioma Wnt signaling and tumor growth by attenuating inhibitory interactions with β-catenin and activating the Wnt pathway. MRI analyses show Merlin-intact meningiomas with S13 phosphorylation and favorable clinical outcomes are associated with high apparent diffusion coefficient (ADC). These results define mechanisms underlying a potential imaging biomarker that could be used to guide treatment de-escalation or imaging surveillance for patients with Merlin-intact meningiomas.

Decoding pediatric spinal tumors: a single-center retrospective case series on etiology, presentation, therapeutic strategies, and outcomes

IntroductionSpinal tumors (ST) often result in dire prognosis, carrying risks such as permanent paralysis, sensory loss, and sphincter dysfunction. Data on their incidence and etiology in pediatric populations are markedly scant. Our study investigates the etiology, clinical manifestation, treatment, and outcomes of pediatric ST.MethodsWe conducted a retrospective review of our institutional pediatric oncology and neurosurgery database, examining 14 patients under 18 years admitted with ST due to oncological diseases since 2005. We analyzed the clinical presentations, evaluations, molecular diagnostics and treatments for these patients.ResultsThe study spanned 15 years and included 14 pediatric patients, each diagnosed with distinct spinal tumor entity. The mean patient age was approximately 19.6 ± 10.1 months. Severe axial pain along the vertebral column was observed in 13 patients, while acute neurological deterioration manifested in 7 patients. As a first-line intervention, 13 patients underwent decompressive surgery through laminectomy and tumor resection, and only one patient received chemotherapy solely. Before surgery, seven patients were unable to walk; post-surgery, six of them regained their ability to ambulate. The diagnosis encompassed a range of neoplasms: two instances of Ewing sarcoma, 3 instances of teratoma, one case presenting an atypical teratoid Rhabdoid tumor, two instances each of low-grade astrocytoma and neuroblastoma, and single instances of ependymoma, meningioma, rhabdomyosarcoma, and embryonal tumors with multilayered rosettes (ETMRs). Three patients succumbed two years after initiating therapy.ConclusionDespite their rarity, intraspinal tumors in pediatric patients pose substantial therapeutic challenges. The intertwined complexities of the disease entity and the patient’s neurological status demand swift initiation of an individualized therapeutic strategy. This crucial step helps optimize outcomes for this patient cohort, who frequently grapple with debilitating health conditions. Inclusion of these patients within a registry is mandatory to optimize treatment outcomes due to their rarity in pediatric population.

Brain tumor classification model using convolutional neural networks on magnetic resonance imaging

Accurate classification of brain tumor images is an important challenge in the field of healthcare, demanding fast and reliable diagnoses for proper treatment. In this article, we propose a model that enhances for the automated classification of brain tumor images brain tumor classification, aiming to support diagnostics, increase confidence, and reduce time in the classification process of meningioma, glioma, and pituitary tumors, in magnetic resonance imaging (MRI) scans. Our results demonstrated that the VGG16 model achieved the best classification results among the three evaluated models. Its deep architecture and learning capacity allowed it to learn more discriminative representations of brain tumors, resulting in a higher accuracy rate. Furthermore, the results of the VGG16 implementation were compared to previous studies representing the state of the art in the field. This comparison highlighted the effectiveness and relevance of the VGG16 model for this classification problem. These results offer valuable insights for future development of automated brain tumor detection systems.

Enhanced brain tumor classification using EfficientNetB0 and SVM with pareto search algorithm optimization

Classifying tumors by type, grade, and stage is crucial for treatment decisions and predicting outcomes. Deep learning, especially Convolutional Neural Networks (CNNs), has significantly advanced tumor classification by effectively analyzing complex patterns in magnetic resonance (MR) images. This work presents a hybrid image classification method using the EfficientNetB0 model and Support Vector Machine (SVM) to categorize brain MR images into pituitary tumor, glioma tumor, meningioma tumor, and normal brain. EfficientNetB0 model extracts deep features from the images, which are then classified by a linear SVM. To significantly enhance classification accuracy for brain images, we use the Pareto algorithm to determine the penalty parameter C for the linear SVM. The testing results showed that the proposed system achieved a classification accuracy of 99.30%, recall of 99.30%, precision of 99.30%, and F1-score of 99.30%, with a high specificity of 99.77%. These results demonstrate that the combination of the Pareto algorithm and SVM significantly contributes to improved classification accuracy for brain images.