Glioma Tumor Research Articles

IMMU-63. MESSENGER RNA CHALLENGE PREDICTS CANCER IMMUNOGENICITY AND RESPONSE TO CHECKPOINT INHIBITORS

Abstract BACKGROUND Checkpoint inhibition has emerged as a promising cancer therapeutic approach. Checkpoint inhibitors (CIs) function by overcoming the adaptive T cell exhaustion induced by solid tumor microenvironment including brain tumors, particularly in the neoadjuvant setting. Unfortunately, not every patient who receives CIs will unlock effective antitumor immunity. Objectives: Microsatellite instability and high mutational burden are considered key criteria to predict response to CIs. However, only a minority of patients will fulfill these criteria suggesting the need to develop innovative technologies to identify CI responsive patients. METHODS To determine whether glioma tumors will respond to CIs, we have developed a novel multilamellar mRNA-lipid particle tool to predict response ex vivo. Messenger RNA is complexed into multilamellar lipid particles to form aggregates (RNA-LPAs) to induce pathogen recognition receptor signaling in tumor cells to define an inflammatory profile as an indirect predictor to CI response. RESULTS In naïve mice, we found that multilamellar RNA-LPAs induce activation of Type I IFN system as supported by decreased secretion of IFN-α in MyD88 KO mice compared with wild type mice. We next selected murine glioma cell lines with known response profile to CIs (responders – GL261 and SMA560 vs non-responders – KR158b and CT2A). Prior to evaluation of cytokine profiling, successful uptake of RNA-LPAs was achieved in all cell lines. In 2D cell lines cultures and their corresponding 3D tumor spheroids, we demonstrated that treatment of CI responsive murine glioma models showed a differential cytokine signature (increased secretion of IFN-β/IL-6/CCL4 versus non-responsive models). CONCLUSION We identified a unique inflammatory signature after ex vivo RNA-LPAs challenge that correlates with checkpoint blockade activity. These findings allow for creation of a diagnostic assay to quickly assess tumor immunogenicity, allowing for informed immunotherapeutic treatment.

IMMU-15. CD74/MIF SIGNALING AXIS DISRUPTION IN BONE MARROW-DERIVED MYELOID CELLS DELAYS MALIGNANT PROGRESSION IN A PRECLINICAL MODEL OF GLIOMA

Abstract INTRODUCTION CD74, a chaperone associated with the major histocompatibility complex class II (MHC-II), plays a crucial role in assembling and transporting MHC-II molecules on the cellular surface. Beyond its physiological function in antigen presentation, CD74 is a negative regulator of inflammation, and its dysregulation is associated with inflammatory disorders and cancer. Notably, tumor stem cells release macrophage migration inhibitory factor (MIF) to trigger CD74 immunosuppressive signaling in tumor-infiltrating immune cells, thereby abrogating CD8+ T cell activation and promoting angiogenesis. While CD74 expression is known in dendritic cells, macrophages and B cells, our recent findings reveal its upregulation in immunosuppressive macrophages within the low-grade glioma (LGG) tumor microenvironment, and its negative correlation with the survival of adolescent and young adult patients with LGG. HYPOTHESIS. We hypothesize that inhibiting CD74/MIF in immunosuppressive myeloid cells may affect tumor progression and prevent malignant transformation (MT) to high-grade glioma (HGG). METHODS To test our hypothesis, we isolated bone marrow cells from immunocompetent RCAS n/tv-a LGG bearing animals, differentiated them into bone marrow-derived myeloid cells (BMDMs), silenced CD74 expression, and co-cultured them with primary CD8+T cells. Moreover, we inhibited CD74/MIF signaling in vivo. RESULTS. Deconvolution analysis of tumor-infiltrating immune cells revealed upregulation of CD74 and its network in myeloid cells during LGG compared to HGG. Silencing CD74 in LGG BMDMs in vitro resulted in the downregulation of genes associated with migration, proliferation, and immunosuppression. The co-culture of LGG CD74KD-BMDMs with CD8+ T cells increased the expression of T cell activation genes. In vivo treatment of LGG mice with MIF inhibitors significantly decreased myeloid cell infiltration in the tumor microenvironment (TME) and prolonged survival. CONCLUSIONS These preclinical studies highlight a role for the CD74/MIF signaling axis in glioma and present a novel immunotherapeutic target to modulate the activity of immunosuppressive myeloid cells in order to delay MT.

DNAR-09. GB13 IS A POTENT NEOADJUVANT TREATMENT OPTION FOR IL13RA2-EXPRESSING GBM AND IMPROVES STANDARD-OF-CARE THERAPY

Abstract Glioblastoma (GBM) is the most common primary adult brain cancer and is uniformly fatal. Despite maximum safe surgical resection followed by radiation and temozolomide chemotherapy, current median survival is 14-18 months. GB13 is a novel therapy for treatment of GBM and received Orphan Drug Designation by the Food and Drug Administration (FDA) for treatment of malignant gliomas. GB13 specifically targets the tumor-restricted IL13Ra2 receptor that is highly expressed in GBM, among other cancers, but not on non-cancerous cells. IL13Ra2 represents a clinically viable target for directed therapies and is being explored by numerous approaches. Previous data demonstrate that GB13 potently kills GBM and diffuse midline glioma (DMG) cells and tumors, IL13Ra2 correlates to GB13 sensitivity and, GB13 can significantly improve the cytotoxic effects of ionizing radiation (IR) when tested in models of pediatric diffuse midline glioma. In the current studies, the ability of GB13 to enhance the effects of IR were explored using IL13Ra2-positive and negative GBM models. Major findings demonstrate that neoadjuvant GB13 treatment increases cytotoxic effects of IR, enhances cell apoptosis and decreases cell viability. These results were not observed in IL13Ra2-negative settings, demonstrating the necessity for this clinical target. Mechanistically, increased cell death was caused by sustained apoptotic signaling through caspase-mediated pathways. Preclinical models support the use of GB13 prior to radiation for decreased tumor proliferation. Using multiple models, our results identify a temporal benefit for treating GBM tumors prior to IR and this should be tested in future clinical studies.

IMMU-37. PRE-TREATMENT INNATE INFLAMMATION ASSOCIATES WITH SURVIVAL AFTER POLIO VIROTHERAPY AND CAN BE MODULATED TO SENSITIZE GLIOMAS TO IN SITU VACCINATION

Abstract Early-stage clinical trials in glioblastoma (GBM) have tested various virotherapy strategies, yet only a subset of patients appear to benefit. We previously demonstrated that a recombinant poliovirus (PVSRIPO) functions by inducing MDA5-dependent, type I interferon (IFN) dominant innate inflammation in myeloid cells to provoke durable and functional antitumor T cell responses in mice. Long-term survival after intratumoral PVSRIPO infusion was observed in a subset of patients with recurrent GBM (rGBM) in two clinical trials. Survival after PVSRIPO was associated with higher pre-treatment intratumoral neutrophil density and MHC-class II gene expression, but also higher pre-treatment inflammatory cytokines in blood. Post-treatment induction of type I/III IFNs detected in blood one day after PVSRIPO infusion also associated with survival of patients with rGBM, suggesting that the proficiency of IFN responses to PVSRIPO may dictate therapy outcome. In human ex vivo glioma tissue assays, only a subset of gliomas mounted type I IFN responses to PVSRIPO and STING agonist treatment, in a manner linked with higher pre-treatment IL-1b levels, and lower CXCL10 induction after treatment. Thus, baseline innate inflammation either influences or reflects the capacity of glioma tumors to respond to virotherapy. Strikingly, in glioma bearing mice, peripheral vaccination against tumor irrelevant vaccines in alum adjuvant (i.e., against antigens not expressed by the tumor) induced bone marrow, spleen and intratumoral innate inflammation; rescued the antitumor efficacy of PVSRIPO in an otherwise refractory glioma model; and improved survival after intratumor STING agonist therapy. Notably, peripheral vaccination induced intratumoral neutrophil and CD4+ T cell inflammation, and increased MHC-class II expression on myeloid cells. These data imply a role for pre-treatment systemic innate immune training in the antitumor efficacy of virotherapy and indicate that modulating either systemic innate inflammation and/or the inflammatory status of bone marrow myeloid progenitors may sensitize gliomas to virotherapy.

EXTH-42. GLIOMA-DERIVED CD200 PROMOTES TUMOR GROWTH AND SUPPRESSES NK CELL AND CD8 T CELL ANTI-GLIOMA ACTIVITY

Abstract Immune-checkpoint inhibitors, such as anti-PD1 and anti-CTLA4, have emerged as promising therapeutic options for several malignancies yet show little efficacy against malignant brain cancers. CD200 is a newly recognized immune-checkpoint which is expressed by a marid of cell types, modulating immune homeostasis through multiple receptors. There is currently limited information regarding CD200 effects in brain tumors. Furthermore, while it is well accepted that CD200 binding to its inhibitory receptor induces a pro-tumorigenic environment through its ability to suppress immune responses, there are increasing evidence that CD200 could also have anti-tumor characteristics. Here we evaluated the role of tumor-derived CD200 on anti-glioma immunity. We demonstrate that CD200 is expressed across glioma types, is shed from tumor cells, and increases over time in serum of patients undergoing immunotherapy. Transcriptomic analysis of CD200 knockout (KO) glioma models reveals that glioma-derived CD200 significantly modifies the glioma tumor microenvironment (TME), not only through immune regulation but also through immune-independent pathways, such as tumor metabolism. Furthermore, we show that CD200 KO gliomas have reduced proliferation and are rejected by their hosts. Downstream analysis revealed that rejection of CD200 KO gliomas relied on a functional adoptive immune system, while decreased proliferation was linked to reduced CXCL10 production by the CD200 KO gliomas. Additionally, we demonstrate that glioma-mediated CD200 strongly suppresses anti-glioma NK cell function within the tumor microenvironment (TME), while secreted CD200 inhibits the priming of antigen-specific CD8 T cells in the lymphatic. Notably, NK cells were essential for the initial rejection of CD200 KO gliomas, while CD8 T cells played a critical role in establishing durable anti-tumor responses. Our work provides new mechanistic insights on glioma-derived CD200-mediated immunosuppression and an untapped potential for targeting CD200 by immunotherapies for malignant gliomas.

EPCO-03. METABOLIC INSIGHT INTO GLIOMA HETEROGENEITY: MAPPING WHOLE EXOME SEQUENCING TOIN VIVO IMAGING WITH STEREOTACTIC LOCALIZATION AND DEEP LEARNING

Abstract Intra-tumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by genomic alterations. While multiparametric imaging captures spatial and functional variations enabling ITH characterization, it falls short in assessing the metabolic activities underpinning phenotypic differences. This gap stems from the challenge of integrating easily accessible, co-located pathology and genomic data with metabolic insights. This study presents a multi-faceted approach combining stereotactic biopsy with clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. The aim is to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma who underwent multiparametric MR scans (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During craniotomy, at least one stereotactic biopsy was collected from each patient, with the screenshots of sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard ROC analyses were used to evaluate detection, with AUC calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75±0.11, with individual AUCs as high as 0.96 for IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. An average AUC of 0.85 across the five mutations was achieved using the combination of T1W, T2W-FLAIR, and ADC. These results suggest the possibility of predicting exome-wide mutation events from non-invasive, in vivo imaging by combining stereotactic localization of glioma samples with a semi-parametric deep learning method. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

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.

An ultrasound-activated piezoelectric sonosensitizer enhances mitochondrial depolarization for effective treatment of orthotopic glioma

Despite the significant advancements in piezoelectric materials for sonodynamic therapy (SDT), the suppression of orthotopic glioma remains challenging, primarily due to the unclear mechanism and the restriction of blood-brain barrier (BBB). Herein, we proposed that layered piezoelectric SrBi2Ta2O9 nanoparticles (SBTO NPs) could effectively depolarize the mitochondrial membrane potential (ΔΨm) of glioma cells under ultrasound (US) exposure. The US-induced band bending in SBTO NPs enhanced redox ability, promoting an increase in reactive oxygen species (ROS) generation. The in vitro results proved that SBTO NPs selectively accumulated in mitochondria under US and induced apoptosis in a mitochondrial depolarization manner mediated by the generation of ROS and free charges. Furthermore, SBTO NPs could cross the BBB and then accumulate in gliomas through US/microbubbles (MBs) procedure and protein-mediated transport. The therapeutic effect of piezoelectric SBTO NPs mediated SDT was proved in the orthotopic glioma mouse model. As validated by the histopathological observation and the long-term evaluation, the good biocompatibility and biosafety of SBTO NPs make it possible for deep tumor therapy, and worthy for further preclinical study. Statement of significanceEmploying piezoelectric sonosensitizers for sonodynamic therapy (SDT) has emerged as a promising strategy for cancer treatment; however, the unclear mechanism and blood-brain barrier (BBB) limit the effectiveness of SDT in glioma. Herein, we developed piezoelectric SrBi2Ta2O9 nanoparticles (SBTO NPs) with a built-in electric field for glioma treatment and explored the underlying therapeutic mechanism. Notably, SBTO NPs selectively accumulated in mitochondria under ultrasound (US) and induced apoptosis in a mitochondrial depolarization manner, which is mediated by the generation of reactive oxygen species (ROS) and free charges. In an orthotopic glioma mouse model, SBTO NPs were delivered into the glioma through US/microbubbles and transferrin-mediated transport pathways, inhibiting tumor growth. This work provides a new paradigm for the treatment of orthotopic glioma and other tumor types.

Role of Glutamine Synthetase on Vascular Permeability in Gliomas.

This study aimed to investigate the effect and underlying mechanism of inhibiting glutamine synthetase (GS) on the vascular permeability of gliomas. C6 glioma rat models were randomly divided into control and L-methionine sulfoximine (MSO) treatment groups. MSO was intraperitoneally injected once every other day for a total of three injections in the MSO group. We assessed the effect of MSO on tumor vascular permeability by tail vein injection of Evans blue dye. GS activity, glutamate (Glu) concentration, glutamine (Gln) concentration, and arginine concentration in tumor tissues were measured using the corresponding kits. qPCR experiments were then conducted to examine the effect of glutamate concentration on N-methyl-D-aspartate (NMDA) receptor expression. Finally, the nitric oxide synthase (NOS) assay kit and the nitric oxide (NO) assay kit were employed to detect NOS activity and NO concentration changes, respectively. Increased glioma tumor vascular permeability was observed after intraperitoneal injection of MSO; MSO acted as an inhibitor of GS, leading to a decrease in GS activity; increased glutamate levels caused activation of NMDA receptors and further activation of NOS; additionally, elevated NO levels were detected in association with an increase in arginine and NOS. Inhibiting GS results in increased vascular permeability in gliomas, which is associated with elevated NO levels and the vasodilatory effects of NO.

Distinguishing clinical and imaging characteristics of primary central nervous system lymphoma from high-grade glioma and metastatic brain tumors.

The purpose of this retrospective analysis was to evaluate the clinical presentations, radiological characteristics, patient outcomes, and therapeutic approaches among individuals diagnosed with primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastatic brain tumors (METS). We assembled a cohort of brain tumor patients from two medical centers, with two oncologists independently reviewing their clinical profiles. A retrospective examination of 87 PCNSL, 87 HGG, and 71 METS cases was performed to assess the aforementioned parameters. Notable variations were identified in the incidence of epileptic seizures and cognitive impairments between PCNSL and METS patients. Cerebral hemisphere involvement was predominantly observed in HGG and METS cases. PCNSL cases exhibited a higher likelihood of multiple lesions, whereas HGG showed a greater tendency for recurrence. The median survival times were established at 24.3months for PCNSL, 44.5months for HGG, and 27.1months for METS patients. In PCNSL cases, the number of lesions was identified as a significant predictor of mortality (P = 0.008). Our findings highlight the importance of clinical and imaging features in diagnosing PCNSL, which may present distinct features compared to HGG and METS.

Intraoperative Ultrasound in Glioma Tumors

Introduction: Glioma tumor is the most common primary brain tumor and extent of resection of this tumor plays a significant role in the survival rate of the patients. Intraoperative ultrasound (IOUS) has become a convenient technique in brain tumor surgery due to its non-invasiveness, affordability, and real-time imaging capabilities, making it appealing to neurosurgeons, and assists neurosurgeons in identifying the tumor's location and adjacent structures during surgery. Methods: This study was done prospectively, in Rasul Akram and Shahada-ye-Haftam Tir hospitals, Tehran, Iran, and evaluated IOUS's use in 20 patients with brain glioma between 2019 and 2022. Simple random sampling was used to select patients. The resection was performed using IOUS, and the extent of resection was assessed through imaging. We used SonoSite convex ultrasound probe with bandwidth of 10-3 MHz and scan depth of 3 to 18 cm. Results: The study found that with ultrasound guidance, the average mass resection rate was 89.3%. The use of ultrasound during surgery improved the resection rate compared to previous studies. Motor complications observed in 15% of patients after surgery included paralysis and verbal deficits, while the rate of meningitis was low. The average length of hospitalization for patients was 10 days, and the average intraoperative bleeding was 344 cubic centimeters. Conclusion: Ultrasound can be a valuable tool in resection of brain glioma as it provides real-time imaging and assists in tumor resection, and detect adjacent structures. However, it should be used in conjunction with other monitoring modalities. Further studies are necessary to explore the full potential and limitations of IOUS in neurosurgery. J Bangladesh Coll Phys Surg 2024; 42: 387-392

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.

TTF-1 immunohistochemistry in primary CNS tumors: A systematic review.

Thyroid transcription factor-1 (TTF-1) is a nuclear protein primarily recognized for its role in the development and differentiation of thyroid, lung, and certain diencephalic tissues. Although well-established as an immunohistochemical marker in thyroid and lung cancers, recent studies have explored its expression and diagnostic value in primary central nervous system (CNS) tumors. This systematic review aims to consolidate current knowledge on TTF-1 immunohistochemistry in primary CNS tumors, assessing its prevalence, diagnostic utility, and clinical implications. The review encompasses various CNS tumor types, including subependymal giant cell astrocytoma, chordoid glioma, pituicytoma, ependymomas, astrocytomas, glioblastomas, medulloblastomas, and choroid plexus tumors, highlighting the potential role of TTF-1 in differentiating these neoplasms from other CNS and metastatic tumors. By synthesizing findings from multiple studies, this review underscores the diagnostic value of TTF-1 in the neuropathological evaluation of CNS tumors and suggests directions for future research to refine its clinical application.

Central nervous system tumors in adolescents and young adults: A Society for Neuro-Oncology consensus review on diagnosis, management, and future directions.

Adolescents and young adults (AYAs; ages 15-39 years) are a vulnerable population facing challenges in oncological care, including access to specialized care, transition of care, unique tumor biology, and poor representation in clinical trials. Brain tumors are the second most common tumor type in AYA, with malignant brain tumors being the most common cause of cancer-related death. The 2021 WHO Classification for central nervous system (CNS) Tumors highlights the importance of integrated molecular characterization with histologic diagnosis in several tumors relevant to the AYA population. In this position paper from the Society for Neuro-Oncology (SNO), the diagnosis and management of CNS tumors in AYA is reviewed, focusing on the most common tumor types in this population, namely glioma, medulloblastoma, ependymoma, and CNS germ cell tumor. Current challenges and future directions specific to AYA are also highlighted. Finally, possible solutions to address barriers in the care of AYA patients are discussed, emphasizing the need for multidisciplinary and collaborative approaches that span the pediatric and adult paradigms of care, and incorporating advanced molecular testing, targeted therapy, and AYA-centered care.

Role of T Lymphocytes in Glioma Immune Microenvironment: Two Sides of a Coin.

Glioma is known for its immunosuppressive microenvironment, which makes it challenging to target through immunotherapies. Immune cells like macrophages, microglia, myeloid-derived suppressor cells, and T lymphocytes are known to infiltrate the glioma tumor microenvironment and regulate immune response distinctively. Among the variety of immune cells, T lymphocytes have highly complex and multifaceted roles in the glioma immune landscape. T lymphocytes, which include CD4+ helper and CD8+ cytotoxic T cells, are known for their pivotal roles in anti-tumor responses. However, these cells may behave differently in the highly dynamic glioma microenvironment, for example, via an immune invasion mechanism enforced by tumor cells. Therefore, T lymphocytes play dual roles in glioma immunity, firstly by their anti-tumor responses, and secondly by exploiting gliomas to promote immune invasion. As an immunosuppression strategy, glioma induces T-cell exhaustion and suppression of effector T cells by regulatory T cells (Tregs) or by altering their signaling pathways. Further, the expression of immune checkpoint inhibitors on the glioma cell surface leads to T cell anergy and dysfunction. Overall, this dynamic interplay between T lymphocytes and glioma is crucial for designing more effective immunotherapies. The current review provides detailed knowledge on the roles of T lymphocytes in the glioma immune microenvironment and helps to explore novel therapeutic approaches to reinvigorate T lymphocytes.

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.

PDOC-09 EVALUATION OF HEALTH SERVICE DELAYS FOR CHILDREN WITH CENTRAL NERVOUS SYSTEM TUMORS AT BUGANDO REFERRAL HOSPITAL MWANZA, TANZANIA

Abstract OBJECTIVE Rapid diagnosis and treatment initiation is crucial for children with central nervous system tumors (CNS). Despite advancement in diagnostic modalities, health service delays persist in many LMICs. The current study evaluates the diagnostic and treatment delays experienced by children presenting to a zonal referral hospital in Mwanza Tanzania, with the goal of identifying breakpoints that can be addressed to improve quality of care through a multidisciplinary approach. METHODS All children age <18yrs with a diagnosis of a CNS tumor from 2020-2023 were included in the analysis. Data was extracted from hospital electronic medical system and clinical files and included demographics, diagnosis, and dates at first presentation to the hospital, diagnosis, surgical intervention, oncology presentation and treatment initiation. Treatment initiation included either surgical resection, chemotherapy or radiotherapy. RESULTS A total of 50 patients were included in the evaluation, with average age of 6.4yrs and equal M: F representation. Most of the cases (80%, n=40) were primary CNS tumors, with 20% metastatic disease. Low grade Glioma (LGG) and high-grade Glioma (HGG) tumors were similarly distributed (n=19, n=21). Among those who had a tumor resection, 56% (n=18) were completed before initial oncology evaluation. Delays were frequently experienced, with the average time from presentation to neurosurgery evaluation of 166 days, time to diagnosis of 121 days, time to oncology evaluation of 139 days, and time to treatment initiation of 127 days. 1yr overall survival for LGG was 67% (n=12/18) and 36% (n=8/22) for HGG, with a treatment abandonment rate of 38% overall (n=19/50). CONCLUSIONS There is significant delay in accessing services for patients presenting with CNS tumors. A multidisciplinary approach and social support of children with CNS tumor is necessary to facilitate early diagnosis and timely treatment to improve quality of care and leads to a higher survival rate.

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.