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Hannah Simpson usually favors certain words in her cancer recovery discussions. The most prized figures of speech include 'miracle,' 'God,' 'blessed,' and 'divine Providence.' It's not surprising that Simpson uses [...]

BACKGROUND: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities. METHODS: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks). RESULTS: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94

Implementation of standardized protocols in neurooncology during the surgical resection of brain tumors is needed to advance the clinical treatment paradigms that use tissue for diagnosis, prognosis, bio-banking, and treatment. Currently recommendations on intraoperative tissue procurement only exist for diffuse gliomas but management of other brain tumor subtypes can also benefit from these protocols. Fresh tissue from surgical resection can now be used for intraoperative diagnostics and functional precision medicine assays. A multidisciplinary neuro-oncology perspective is critical to develop the best avenues for practical standardization. This perspective from the multidisciplinary Oncology Tissue Advisory Board (OTAB) discusses current advances, future directions, and the imperative of adopting standardized protocols for diverse brain tumor entities. There is a growing need for consistent operating room practices to enhance patient care, streamline research efforts, and optimize outcomes.

The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Background: Non-invasive brain mapping using navigated transcranial magnetic stimulation (nTMS) is a valuable tool prior to resection of malignant brain tumors. With nTMS motor mapping, it is additionally possible to analyze the function of the motor system and to evaluate tumor-induced neuroplasticity. Distinct changes in motor cortex excitability induced by certain malignant brain tumors are a focal point of research. Methods: A retrospective single-center study was conducted involving patients with malignant brain tumors. Clinical data, resting motor threshold (rMT), and nTMS-based tractography were evaluated. The interhemispheric rMT-ratio (rMTTumor/rMTControl) was calculated for each extremity and considered pathological if it was >110% or <90%. Distances between the corticospinal tract and the tumor (lesion-to-tract-distance -- LTD) were measured. Results: 49 patients were evaluated. 16 patients (32.7%) had a preoperative motor deficit. The cohort comprised 22 glioblastomas (44.9%), 5 gliomas of Classification of Tumors of the Central Nervous System (CNS WHO) grade 3 (10.2%), 6 gliomas of CNS WHO grade 2 (12.2%) and 16 cerebral metastases (32.7%). 26 (53.1%) had a pathological rMT-ratio for the upper extremity and 35 (71.4%) for the lower extremity. All patients with tumor-induced motor deficits had pathological interhemispheric rMT-ratios, and presence of tumor-induced motor deficits was associated with infiltration of the tumor to the nTMS-positive cortex (p = 0.04) and shorter LTDs (all p<0.021). Pathological interhemispheric rMT-ratio for the upper extremity was associated with cerebral metastases, but not with gliomas (p = 0.002). Conclusions: Our study underlines the diagnostic potential of nTMS motor mapping to go beyond surgical risk stratification. Pathological alterations in motor cortex excitability can be measured with nTMS mapping. Pathological cortical excitability was more frequent in cerebral metastases than in gliomas. [ABSTRACT FROM AUTHOR]

The blood-brain barrier (BBB) is a selectively semi-permeable layer, crucial in shielding the brain from external pathogens and toxic substances while maintaining ionic homeostasis and sufficient nutrient supply. However, it poses a significant challenge for drugs to penetrate the BBB in order to effectively target brain tumors. Magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT) is a minimally invasive technique that employs thermal energy to cauterize intracranial lesions with the potential to temporarily disrupt the BBB. This further opens a possible therapeutic window to enhance patient outcomes. Here, we review the impact of MRg-LITT on BBB and blood tumor barrier (BTB) and the duration of the BBB disruption. Studies have shown that MRg-LITT is effective due to its minimally invasive nature, precise tumor targeting, and low complication rates. Although the disruption duration varies across studies, the average peak disruption is within the initial two weeks post-ablation period and subsequently exhibits a gradual decline. However, further research involving larger groups with extended follow-up periods is required to determine disruption duration more accurately. In addition, evaluating toxicity and glymphatic system disruption is crucial to circumvent potential risks associated with this procedure. [ABSTRACT FROM AUTHOR]

Background and purpose: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Materials and methods: Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Results: Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. Conclusion: The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma. [ABSTRACT FROM AUTHOR]

To improve immunotherapy for brain tumors, it is important to determine the principal intracranial site of T cell recruitment from the bloodstream and their intracranial route to brain tumors. Using intravital microscopy in mouse models of intracranial melanoma, we discovered that circulating T cells preferably adhered and extravasated at a distinct type of venous blood vessel in the tumor vicinity, peritumoral venous vessels (PVVs). Other vascular structures were excluded as alternative T cell routes to intracranial melanomas. Anti-PD-1/CTLA-4 immune checkpoint inhibitors increased intracranial T cell motility, facilitating migration from PVVs to the tumor and subsequently inhibiting intracranial tumor growth. The endothelial adhesion molecule ICAM-1 was particularly expressed on PVVs, and, in samples of human brain metastases, ICAM-1 positivity of PVV-like vessels correlated with intratumoral T cell infiltration. These findings uncover a distinct mechanism by which the immune system can access and control brain tumors and potentially influence other brain pathologies. [Display omitted] • PVVs are key structures for T cell recruitment to melanoma brain tumors • Anti-PD-1/CTLA-4 inhibitors boost T cell recruitment through PVVs • T cell recruitment and antitumor immunity is dependent on ICAM-1 expression on PVVs • ICAM-1 on PVVs correlates with T cell infiltration in human melanoma brain metastases How T cells are recruited to brain tumors from the blood remains unclear. Messmer et al. identify peritumoral venous vessels (PVVs) as key structures for T cell recruitment to melanoma brain tumors. PVVs are the sites of T cell extravasation and facilitated rapid T cell migration under immune checkpoint inhibition. T cell recruitment and antitumor immunity were dependent on ICAM-1. [ABSTRACT FROM AUTHOR]

[Display omitted] • Comprehensive analysis of novel treatment modalities for Glioblastoma (GBM). • Role of nanocarriers in addressing limitations with conventional GBM therapeutics. • Updates of clinical landscape of novel GBM therapeutics. • Limitations with novel nanoparticles approach in GBM therapeutics. Glioblastoma multiforme (GBM) represents one of the most prevailing and aggressive primary brain tumors among adults. Despite advances in therapeutic approaches, the complex microenvironment of GBM poses significant challenges in its optimal therapy, which are attributed to immune evasion, tumor repopulation by stem cells, and limited drug penetration across the blood–brain barrier (BBB). Nanotechnology has emerged as a promising avenue for GBM treatment, offering biosafety, sustained drug release, enhanced solubility, and improved BBB penetrability. In this review, a comprehensive overview of recent advancements in nanocarrier-based drug delivery systems for GBM therapy is emphasized. The conventional and novel treatment modalities for GBM and the potential of nanocarriers to overcome existing limitations are comprehensively covered. Furthermore, the updates in the clinical landscape of GBM therapeutics are presented in addition to the current status of drugs and patents in the same context. Through a critical evaluation of existing literature, the therapeutic prospect and limitations of nanocarrier-based drug delivery strategies are highlighted offering insights into future research directions and clinical translation. [ABSTRACT FROM AUTHOR]

An accurate diagnosis of a brain tumour in its early stages is required to improve the possibility of survival for cancer patients. Due to the structural complexity of the brain, it has become very difficult and tedious for neurologists and radiologists to diagnose brain tumours in the initial stages with the help of various common manual approaches to tumour diagnosis. To improve the performance of the diagnosis, some computer-aided diagnosis-based systems are developed with the concepts of artificial intelligence. In this proposed manuscript, we analyse various computer-aided design (CAD)-based approaches and design a modern approach with ideas of transfer learning over deep learning on magnetic resonance imaging (MRI). In this study, we apply a transfer learning approach with the object detection model YOLO (You Only Look Once) and analyse the MRI dataset with the various modified versions of YOLO. After the analysis, we propose an object detection model based on the modified YOLOv7 with a morphological filtering approach to reach an efficient and accurate diagnosis. To enhance the performance accuracy of this suggested model, we also analyse the various versions of YOLOv7 models and find that the proposed model having the YOLOv7-E6E object detection technique gives the optimum value of performance indicators as precision, recall, F1, and mAP@50 as 1, 0.92, 0.958333, and 0.974, respectively. The value of mAP@50 improves to 0.992 by introducing a morphological filtering approach before the object detection technique. During the complete analysis of the suggested model, we use the BraTS 2021 dataset. The BraTS 2021 dataset has brain MR images from the RSNA-MICCAI brain tumour radiogenetic competition, and the complete dataset is labelled using the online tool MakeSense AI. [ABSTRACT FROM AUTHOR]

The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly and accurately developing and training AI models for detecting and classifying fabric defects in real-life applications. Models trained on limited datasets struggle to identify underrepresented defects, reducing their practicality. To address these issues, this study suggests using a conditional generative adversarial network (cGAN) for fabric defect data augmentation. The proposed image-to-image translator GAN features a conditional U-Net generator and a 6-layered PatchGAN discriminator. The conditional U-Network (U-Net) generator can produce highly realistic synthetic defective samples and offers the ability to control various characteristics of the generated samples by taking two input images: a segmented defect mask and a clean fabric image. The segmented defect mask provides information about various aspects of the defects to be added to the clean fabric sample, including their type, shape, size, and location. By augmenting the training dataset with diverse and realistic synthetic samples, the AI models can learn to identify a broader range of defects more accurately. This technique helps overcome the limitations of small or unvaried datasets, leading to improved defect detection accuracy and generalizability. Moreover, this proposed augmentation method can find applications in other challenging fields, such as generating synthetic samples for medical imaging datasets related to brain and lung tumors. [ABSTRACT FROM AUTHOR]

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Objective: Sexual functioning is a multifaceted aspect of human life that can be profoundly affected in patients with glioma. Most frequent symptoms include reduced sexual desire, difficulties in sexual arousal, or low satisfaction. Such symptoms may cause distress or interpersonal difficulties, inevitably resulting in negative outcomes on different domains of patients' quality of life. Despite this, sexuality is rarely addressed by medical staff and remains understudied. An important question still unanswered is whether sexual dysfunctions in glioma patients correlate with features of the tumor itself, with its treatment, or with the secondary effects of the tumor on the patient's psychological status. To answer this question, the present study aims to investigate the incidence of sexual life impairments in a very large population of patients with low- and high-grade gliomas, focusing on demographic, clinical, and treatment factors associated with their occurrence and developments. Methods: A total of 148 patients treated for glioma were evaluated for sexual functioning, i.e., sexual dysfunction (SD), relationship status (RS), intercourse frequency (IF), and sexual satisfaction (SS), by using a specific anonymous questionnaire. Descriptive statistics were utilized to investigate participant characteristics and to evaluate the occurrence of sexual problems. Chi-squared tests were performed to detect the association between "SS" or "IF" and different clinical/demographic factors as well as between "SS" or "IF" and the "subjective–personal skills judgment". Results: Results showed no difference between male and female patients, a very low frequency (1.4%) of SD, but a consistent percentage (25%) of subjective deterioration in sexual wellbeing. Notably, 24% of patients reported to have interrupted their relationship after the diagnosis. Chi-squared analyses reveal an association between adjuvant treatments (chemotherapy and radiotherapy) and reduction of IF. Interestingly, "SS" or "IF" was not associated with demographic, clinical, or histomolecular factors. Conclusion: Our study showed that sexual problems in glioma patients are not uncommon, and they are especially linked to SS, RS, and IF. Specifically, intercourse frequency reduction is associated with the adjuvant treatments. Results highlight the need for improved assessment strategies and interventions tailored to the unique needs of brain tumor patients. [ABSTRACT FROM AUTHOR]

\nPlain Language SummaryThe brain is a crucial organ that controls the body’s neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) is implemented here for effective segmentation and classification. The TFHO is the merging of the Taylor series and Fire Hawk Optimizer (FHO). The de-noising is accomplished by the adaptive median filter, and the segmentation is carried out using M-Net, which has been trained by TFHO. Subsequently, image augmentation is performed to increase the image dimension, followed by the extraction of effective features. Finally, DenseNet is used for the classification, and the training is done by TFHO. The introduced method obtained 94.86% accuracy, 92.83% Negative Predictive Values, 89.33% Positive Predictive Values (PPV), 95.91% True Positive Rate (TPR), 4.37% False Negative Rate (FNR), and 90.98% F1-score.In this research, the TFHO-DenseNet is developed for the timely diagnosis and accurate classification of brain tumor. This is utilized for the early tumor’s detection, which is essential to provide significant treatment to patients and enhance the survival rate. [ABSTRACT FROM AUTHOR]

The application of chimeric antigen receptor T-cell therapy in central nervous system tumors has significantly advanced; however, challenges pertaining to the blood-brain barrier, immunosuppressive microenvironment, and antigenic heterogeneity continue to be encountered, unlike its success in hematological malignancies such as acute lymphoblastic leukemia and diffuse large B-cell lymphomas. This review examined the research progress of chimeric antigen receptor T-cell therapy in gliomas, medulloblastomas, and lymphohematopoietic tumors of the central nervous system, focusing on chimeric antigen receptor T-cells targeting antigens such as EGFRvIII, HER2, B7H3, GD2, and CD19 in preclinical and clinical studies. It synthesized current research findings to offer valuable insights for future chimeric antigen receptor T-cell therapeutic strategies for central nervous system tumors and advance the development and application of this therapeutic modality in this domain. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most commonly occurring and most aggressive primary brain tumor. Transcriptomics-based tumor subtype classification has established the mesenchymal lineage of GBM (MES-GBM) as cancers with particular aggressive behavior and high levels of therapy resistance. Previously it was show that Trihexyphenidyl (THP), a market approved M1 muscarinic receptor-targeting oral drug can suppress proliferation and survival of GBM stem cells from the classical transcriptomic subtype. In a series of in vitro experiments, this study confirms the therapeutic potential of THP, by effectively suppressing the growth, proliferation and survival of MES-GBM cells with limited effects on non-tumor cells. Transcriptomic profiling of treated cancer cells identified genes and associated metabolic signaling pathways as possible underlying molecular mechanisms responsible for THP-induced effects. In vivo trials of THP in immunocompromised mice carry orthotopic MES-GBMs showed moderate response to the drug. This study further highlights the potential of THP repurposing as an anti-cancer treatment regimen but mode of action and d optimal treatment procedures for in vivo regimens need to be investigated further. [ABSTRACT FROM AUTHOR]

Targeted drug delivery for primary brain tumors, particularly gliomas, is currently a promising approach to reduce patient relapse rates. The use of substitutable scaffolds, which enable the sustained release of clinically relevant doses of anticancer medications, offers the potential to decrease the toxic burden on the patient's organism while also enhancing their quality of life and overall survival. Upconversion nanoparticles (UCNPs) are being actively explored as promising agents for detection and monitoring of tumor growth, and as therapeutic agents that can provide isolated therapeutic effects and enhance standard chemotherapy. Our study is focused on the feasibility of constructing scaffolds using methacrylated hyaluronic acid with additional impregnation of UCNPs and the chemotherapeutic drug temozolomide (TMZ) for glioma treatment. The designed scaffolds have been demonstrated their efficacy as a drug and UCNPs delivery system for gliomas. Using the aggressive orthotopic glioma model in vivo , it was found that the scaffolds possess the capacity to ameliorate neurological disorders in mice. Moreover, upon intracranial co-implantation of the scaffolds and glioma cells, the constructs disintegrate into distinct segments, augmenting the release of UCNPs into the surrounding tissue and concurrently reducing postoperative damage to brain tissue. The use of TMZ in the scaffold composition contributed to restraining glioma development and the reduction of tumor invasiveness. Our findings unveil promising prospects for the application of photopolymerizable biocompatible scaffolds in the realm of neuro-oncology. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to assess the diagnostic management and follow‐up imaging for glioma patients across Belgian hospitals by calculating process indicators. Methods: Patients with newly diagnosed glioma in Belgium (2016–2019) were selected from the Belgian Cancer Registry. The National Social Security Number served as unique patient identifier, linking the Registry to vital status and reimbursement data. Nine measurable process related to diagnosis and follow‐up imaging were identified, with reformulations for 7 due to data limitations. For each indicator, technical documentation sheets, containing all required details (rationale, numerator and denominator, target, limitations, benchmarking, subgroup analyses) were developed, reviewed by a multidisciplinary expert panel, and validated in six pilot hospitals. Per indicator, patients were assigned to the most relevant hospital per indicator using allocation algorithms. Results: Results for process indicators assessing MRI use in glioma diagnosis and follow‐up aligned with predefined targets (90%), except for early postoperative MRI (48.5% vs. target 90%). Mandatory reporting of the WHO performance status (89.3% vs. target 100%) and performance of full‐spine (43.6% vs. target 90%) and follow‐up MRI (73.5% vs. target 90%) in ependymoma were suboptimal. The largest variability across centers was noted for the indicator on early postoperative MRI. Conclusion: This calculation of process indicators identified opportunities for improvement in diagnosis and follow‐up imaging for glioma patients in Belgium. Monitoring indicator results and providing individual feedback reports to the Belgian hospitals invites neuro‐oncology care teams and hospital managements to reflect on their results and to take measures to continuously improve care for glioma. [ABSTRACT FROM AUTHOR]

Glioblastoma is one of the most aggressive and treatment‐resistant forms of primary brain cancer, posing significant challenges in effective therapy. This study aimed to enhance the effectiveness of glioblastoma therapy by developing a unique nanomedicine composed of Pluronic F127‐complexed PEGylated poly(glutamic acid)‐cisplatin (PLG‐PEG/PF127‐CDDP). PLG‐PEG/PF127‐CDDP demonstrated an optimal size of 133.97 ± 12.60 nm, facilitating efficient cell uptake by GL261 glioma cells. In vitro studies showed significant cytotoxicity against glioma cells with a half‐maximal (50%) inhibitory concentration (IC50) of 12.61 µg mL−1 at 48 h and a 72.53% ± 1.89% reduction in cell invasion. Furthermore, PLG‐PEG/PF127‐CDDP prolonged the circulation half‐life of cisplatin to 9.75 h in vivo, leading to a more than 50% reduction in tumor size on day 16 post‐treatment initiation in a murine model of glioma. The treatment significantly elevated lactate levels in GL261 cells, indicating enhanced metabolic disruption. Therefore, PLG‐PEG/PF127‐CDDP offers a promising approach for glioblastoma therapy due to its effects on improving drug delivery efficiency, therapeutic outcomes, and safety while minimizing systemic side effects. This work underscores the potential of polymer‐based nanomedicines in overcoming the challenges of treating brain tumors, paving the way for future clinical applications. [ABSTRACT FROM AUTHOR]

Background: High hypericin-loaded polyvinylpyrrolidone (HHL-PVP) constitutes a novel approach to utilize the promising characteristics of hypericin for photodynamic diagnosis (PDD) and therapy (PDT) of brain tumors in an orally bioavailable formulation. The aim of this study was to investigate the ability of a Complementary Metal-Oxide-Semiconductor (CMOS) camera-based fluorescence imaging system to selectively visualize HHL-PVP in glioblastoma tissue even in the presence of 5-Aminolvevulinic acid (5-ALA) induced fluorescence, which is widely utilized in brain tumor surgery. Methods: We applied a previously established system with a non-hypericin specific filter for 5-ALA fluorescence visualization and a newly introduced hypericin-specific filter at 575–615 nm that transmits the spectrum of hypericin, but not 5-ALA fluorescence. Glioblastoma specimens obtained from 12 patients (11 with preoperative 5-ALA intake) were ex vivo incubated with HHL-PVP. Subsequently, fluorescence intensity and lifetime changes using both the non-hypericin specific filter and hypericin-specific filter were measured before and after HHL-PVP incubation and after subsequent rinsing. Results: While no significant differences in fluorescence signal were observed using the non-hypericin specific filter, statistically significant increases in fluorescence intensity (p = 0.001) and lifetime (p = 0.028) after HHL-PVP incubation were demonstrated using the hypericin-specific filter. In consequence, specimens treated with HHL-PVP could be identified according to the fluorescence signal with high diagnostic sensitivity (87.5%) and specificity (100%). Conclusions: Our CMOS camera-based system with a hypericin-specific filter is capable of selectively visualizing hypericin fluorescence in glioblastoma tissue after ex vivo HHL-PVP incubation. In the future, this technique could facilitate clinical investigations of HHL-PVP for PDD and PDT while maintaining the current standard of care with 5-ALA guidance. [ABSTRACT FROM AUTHOR]

Glioblastoma is the most fatal and common malignant brain tumor, excluding metastasis and with a median survival of approximately one year. While solid tumors benefit from newly approved drugs, immunotherapy, and prevention, none of these scenarios are opening for glioblastoma. The key to unlocking the peculiar features of glioblastoma is observing its molecular and anatomical features tightly entangled with the host's central nervous system (CNS). In June 2024, we searched the PUBMED electronic database. Data collection and analysis were conducted independently by two reviewers. Results: A total of 215 articles were identified, and 192 were excluded based on inclusion and exclusion criteria. The remaining 23 were used for collecting divergent molecular pathways and anatomical features of glioblastoma. The analysis of the selected papers revealed a multifaced tumor with extreme variability and cellular reprogramming that are observable within the same patient. All the variability of glioblastoma could be clustered into three pillars to dissect the physiology of the tumor: 1. necrotic core; 2. vascular proliferation; 3. CNS infiltration. These three pillars support glioblastoma survival, with a pivotal role of the neurovascular unit, as supported by the most recent paper published by experts in the field. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with advanced stages of breast cancer are at high risk of the cancer metastasizing or spreading to areas of the central nervous system, including the brain, cerebrospinal fluid, and the protective tissue layers of the brain, termed leptomeninges. Although guidelines recommend screening for central nervous system disease in other cancers, such as non-small cell lung cancer, there are no such recommendations for patients with breast cancer unless they have symptoms such as headaches, weakness, or vomiting. This review discusses the evidence behind screening for breast cancer that has spread to the central nervous system, as well as new methods of diagnosis, including specialized imaging, serum testing, and cerebrospinal fluid analysis. Brain and leptomeningeal metastases are complications of breast cancer with high rates of morbidity and mortality and have an estimated incidence of up to 30%. While National Comprehensive Cancer Network (NCCN) guidelines recommend screening for central nervous system metastasis in other neurotropic cancers such as non-small cell lung cancer, there are no such recommendations for asymptomatic breast cancer patients at any stage of disease. This review highlights ongoing studies into screening and diagnostics for breast cancer with brain and leptomeningeal metastasis (BCBLM) as they relate to patient outcomes and prognostication. These include imaging methods such as MRI with novel contrast agents with or without PET/CT, as well as 'liquid biopsy' testing of the cerebrospinal fluid and serum to analyze circulating tumor cells, genomic material, proteins, and metabolites. Given recent advances in radiation, neurosurgery, and systemic treatments for BCBLM, screening for CNS involvement should be considered in patients with advanced breast cancer as it may impact treatment decisions and overall survival. [ABSTRACT FROM AUTHOR]

Simple Summary: In brain tumor surgery, increasing the extent of resection has been shown to improve patient outcomes and survival. 5-ALA serves as a tumor visualization adjunct and has been approved for use in adult high-grade gliomas. The purpose of this systematic review was to assess whether 5-ALA has similar utility and safety in pediatric patients. A total of 249 pediatric cases were identified from the relevant literature, confirming the safety of 5-ALA in this population. Overall, the fluorescence rates and utility were favorable, although there were some variations across different tumor grades and types. While our preliminary findings suggest that 5-ALA is both safe and effective in pediatric brain tumor surgery, further systematic clinical studies are needed to validate these results. Background: 5-Aminolevulinic acid-guided surgery for adult gliomas has been approved by the European Medicines Agency and the US Food and Drug Administration, becoming a reliable tool for improving gross total resection rates and patient outcomes. This has led several medical centers to explore the off-label use of 5-ALA in the resection of pediatric brain tumors, assessing its efficacy and safety across various tumor types. However, given the differences between children and adults, the appropriateness of 5-ALA use in pediatric populations has not yet been fully established. Methods: We collected eligible publications from Embase, Scopus, PubMed, and Proquest, ultimately selecting 27 studies. Data extraction and retrospective analysis of 249 surgical cases were conducted to determine the current efficacy and safety of 5-ALA in pediatric brain tumors. The fluorescence rate and utility stratified by several clinical features, including WHO grade, tumor classification, and tumor location, were analyzed. Results: Most studies suggest that 5-ALA can enhance tumor identification in high-grade tumors, including glioblastomas and anaplastic astrocytomas. Changes in survival or recurrence rates associated with 5-ALA-guided resection have not been reported. None of the cases reported significant postoperative complications related to the use of 5-ALA. Conclusions: 5-ALA can aid in the resection of high-grade gliomas in pediatric patients. The efficacy of 5-ALA in low-grade gliomas and other tumors may require enhancement with additional tools or modified administration protocols. The safety of 5-ALA has reached a preliminary consensus, although further randomized controlled trials and data on survival and molecular characteristics are needed. [ABSTRACT FROM AUTHOR]

Simple Summary: Proton beam radiation therapy is one of the major treatment modalities used for cancer treatment, including brain tumors. This treatment modality is distinct from other radiation options because of its ability to deliver radiation to tumor targets while sparing healthy tissue. Brainstem toxicity is a rare but important complication can arise due to exposure to radiation; hence, in this systematic review and meta-analysis, we aimed to explore the risk of brainstem toxicity in pediatric brain tumor patients undergoing proton beam radiation, focusing on quantifying its incidence and severity. Eleven articles were considered eligible in our study, and the results yielded an overall brainstem toxicity incidence of 1.8%, ranging in severity, with Grade 1 brainstem toxicity (asymptomatic) being the most common. This study revealed a low incidence of symptomatic brainstem toxicity and related mortality among pediatric brain tumor patients undergoing proton beam radiation, which could support the idea of it having a good toxicity profile and possibly re-enforces the need for more comprehensive primary studies regarding this radiation modality in brain tumor patients to uncover unknowns and help us understand the grey areas of this topic. Background: Proton beam radiation therapy (PBRT) is an advanced cancer treatment modality that utilizes the distinctive physical properties of protons to precisely deliver radiation to tumor targets while sparing healthy tissue. This cannot be obtained with photon radiation. In this systematic review and meta-analysis, we aimed to comprehensively assess the risk of brainstem toxicity in pediatric brain tumor patients undergoing PBRT. Methods: With adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a predetermined search strategy was used to identify eligible articles from PubMed, Web of Science, Scopus, and Cochrane Library through July 2024. Results: The current study included a total of 11 eligible articles. The pooled prevalence of patients who suffered from brainstem toxicity was 1.8% (95% CI: 1%, 2.6%). The pooled prevalences of patients with Grade 1 to Grade 5 brainstem toxicity were found to be 10.6% (95% CI: 8.8%, 30%), 1.5% (95% CI: 0.6%, 2.5%), 0.7% (95% CI: 0.3%, 1.1%), 0.4% (95% CI: 0.1%, 0.7%), and 0.4% (95% CI: 0.1%, 0.8%), respectively, with an overall pooled prevalence of 0.7% (95% CI: 0.4%, 1%). Conclusions: This study revealed a relatively low incidence of symptomatic brainstem toxicity and its related mortality in the pediatric population undergoing PBRT. However, further research is encouraged to study the broader effects of PBRT and to explore various factors that may influence the risk of brainstem toxicity in patients treated with PBRT. [ABSTRACT FROM AUTHOR]

Purpose: Pediatric brain tumor survivors can experience detrimental effects from radiation treatment. This cross-sectional, large cohort study examined late psychosocial and executive functioning effects in pediatric patients treated ≥ 3 years after proton radiation therapy (PRT). Methods: Parents of 101 pediatric brain tumor survivors completed the Behavior Assessment System for Children and the Behavior Rating Inventory of Executive Function. Standard scores were compared to published normative means, rates of impairment (T-score > 65) were calculated, and demographic and clinical characteristics were examined. Results: Mean age at PRT was 8.12 years and mean interval from PRT to assessment was 6.05 years. Half were female (49.5%), 45.5% received craniospinal irradiation (CSI), and 58.4% were diagnosed with infratentorial tumors. All mean T-scores were within normal range. Mean T-scores were significantly elevated compared to the norm on the withdrawal, initiate, working memory, and plan/organize scales. Rates of impairment were notably high in working memory (24.8%), initiate (20.4%), withdrawal (18.1%), and plan/organize (17.0%). Greater withdrawal was significantly associated with CSI and also with chemotherapy and diagnosis of hearing loss. Mean T-scores were significantly lower than the norm on the hyperactivity, aggression, conduct problems, and inhibition scales. No significant problems were identified with social skills or depression. Interval since treatment was not correlated with any scale. Conclusion: Although psychosocial and executive functioning was within the normal range, on average, social withdrawal and metacognitive executive functioning (working memory, initiating, planning/organizing) were areas of concern. Targeted yearly screening and proactive executive skill and social interventions are needed for this population. [ABSTRACT FROM AUTHOR]

Objective: Cranial computed tomography (CT) scan is the most widely used tool to rule out intracranial lesions after pediatric traumatic brain injury (TBI). However, in pediatric population, the radiation exposure can lead to an increased risk of hematological and brain neoplasm. Defined in 2019 National Institute for Health and Care Excellence (NICE) guidelines as "troponins for the brain", serum biomarkers measurements, particularly S100B, have progressively emerged as a supplementary tool in the management of TBI thanks to their capacity to predict intracranial post-traumatic lesions. Methods: This systematic review was conducted following the PRISMA protocol (preferred reporting items for systematic reviews and meta-analyses). No chronological limits of study publications were included. Studies reporting data from children with TBI undergoing serum S100B measurement and computed tomography (CT) scans were included. Results: Of 380 articles screened, 10 studies met the inclusion criteria. Patients admitted with mild-TBI in the Emergency Department (ED) were 1325 (80.25%). The overall pooled sensitivity and specificity were 98% (95% CI, 92–99%) and 45% (95% CI, 29–63%), respectively. The meta-analysis revealed a high negative predictive value (NVP) (99%; 95% CI, 94–100%) and a low positive predictive value (PPV) (41%; 95% CI, 16–79%). Area under the curve (AUC) was 76% (95% CI, 65–85%). The overall pooled negative predictive value (NPV) was 99% (95% CI, 99–100%). Conclusions: The measurement of serum S100B in the diagnostic workflow of mTBI could help informed decision-making in the ED setting, potentially safely reducing the use of CT scan in the pediatric population. The high sensitivity and excellent negative predictive values look promising and seem to be close to the values found in adults. Despite this, it must be pointed out the high heterogeneity (> 90%) found among studies. In order for S100B to be regularly introduced in the pediatric workflow for TBI, it is important to conduct further studies to obtain cut-off levels based on pediatric reference intervals. [ABSTRACT FROM AUTHOR]

Background: Tuberculomas are prevalent in developing countries and demonstrate variable signals on MRI resulting in the overlap of the conventional imaging phenotype with other entities including glioma and brain metastasis. An accurate MRI diagnosis is important for the early institution of anti-tubercular therapy, decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision. This study aims to assess the potential of radiomics features of regular contrast images including T1W, T2W, T2W FLAIR, T1W post contrast images, and ADC maps, to differentiate between tuberculomas, high-grade-gliomas and metastasis, the commonest intra parenchymal mass lesions encountered in the clinical practice. Methods: This retrospective study includes 185 subjects. Images were resampled, co-registered, skull-stripped, and zscore-normalized. Automated lesion segmentation was performed followed by radiomics feature extraction, train-test split, and features reduction. All machine learning algorithms that natively support multiclass classification were trained and assessed on features extracted from individual modalities as well as combined modalities. Model explainability of the best performing model was calculated using the summary plot obtained by SHAP values. Results: Extra tree classifier trained on the features from ADC maps was the best classifier for the discrimination of tuberculoma from high-grade-glioma and metastasis with AUC-score of 0.96, accuracy-score of 0.923, Brier-score of 0.23. Conclusion: This study demonstrates that radiomics features are effective in discriminating between tuberculoma, metastasis, and high-grade-glioma with notable accuracy and AUC scores. Features extracted from the ADC maps surfaced as the most robust predictors of the target variable. [ABSTRACT FROM AUTHOR]

Purpose: Cachexia is a complex syndrome characterized by unintentional weight loss, progressive muscle wasting and loss of appetite. Anti-Fn14 antibody (mAb 002) targets the TWEAK receptor (Fn14) in murine models of cancer cachexia and can extend the lifespan of mice by restoring the body weight of mice. Here, we investigated glucose metabolic changes in murine models of cachexia via [18F]FDG PET imaging, to explore whether Fn14 plays a role in the metabolic changes that occur during cancer cachexia. Methods: [18F]FDG PET/MRI imaging was performed in cachexia-inducing tumour models versus models that do not induce cachexia. SUVaverage was calculated for all tumours via volume of interest (VOI) analysis of PET/MRI overlay images using PMOD software. Results: [18F]FDG PET imaging demonstrated increased tumour and brain uptake in cachectic versus non-cachectic tumour-bearing mice. Therapy with mAb 002 was able to reduce [18F]FDG uptake in tumours (P < 0.05, n = 3). Fn14 KO tumours did not induce body weight loss and did not show an increase in [18F]FDG tumour and brain uptake over time. In non-cachectic mice bearing Fn14 KO tumours, [18F]FDG tumour uptake was significantly lower (P < 0.01) than in cachectic mice bearing Fn14 WT counterparts. As a by-product of glucose metabolism, l-lactate production was also increased in cachexia-inducing tumours expressing Fn14. Conclusion: Our results demonstrate that Fn14 receptor activation is linked to glucose metabolism of cachexia-inducing tumours. [ABSTRACT FROM AUTHOR]

Glioblastomas (GBM) are most common types of primary brain tumors and miRNAs play an important role in pathogenesis of glioblastomas. Here, we reported a new miRNA, miR-3154, which regulates glioblastoma proliferation and metastasis. miR-3154 was elevated in glioblastoma tissue and cell lines, and its elevation was associated with grade of glioblastomas. Knockdown of miR-3154 in cell lines weakened ability of proliferation and colony formation, and caused cell cycle arrested and higher percentage of apoptosis. Knockdown of miR-3154 also impaired ability of migration and invasion in glioblastoma cells. In mechanism, miR-3154 bound directly to Tumor Protein P53 Inducible Nuclear Protein 1 (TP53INP1), down-regulating TP53INP1 expression at both mRNA and protein level. Silence of TP53INP1 reversed the effect of miR-3154 knockdown on proliferation and metastasis of glioblastoma cells. These findings show that miR-3154 promotes glioblastoma proliferation and metastasis via targeting TP53INP1. [ABSTRACT FROM AUTHOR]

Objective: In the past, brain stem was treated with surgery or placement of shunt in Pakistan. Gamma Knife surgery is currently an alternative to surgery for deep brain lesions. In the current study, we show the clinical experience of our Centre treated with Gamma Knife surgery Methods: This is a descriptive study conducted between February 2016 and October 2021. We had total 47 patients presented with focal brainstem gliomas which were selected for Gamma knife radiosurgery at the Neurospinal and Cancer Care Institute Karachi. Results: Clinical Response was observed among them 20 (42.55%) patients improved, 22(46.80%) were stable, while 05 (10.63%) got worse The mean duration of symptoms was 31.9(SD10.5± 3months). Karnofsky Performance Status (KPS) scores during Gamma knife radiosurgery were 90 in 24 patients (51%), 80 in 21 patients (44.6%), and 70 in two patients (4.2%). Four patients (10.6%) had received conventional radiotherapy before Gamma knife radiosurgery. Conclusion: Among the diverse gliomas, their peril varies not just by type but also by their intricate location within the brain. The efficacy of gamma knife radiation is excellent particularly when tackling high-grade tumors, exhibiting its prowess in both adult and pediatric cases. [ABSTRACT FROM AUTHOR]

• Dynamic contrast MRI can be used to describe diffusive and convective flow in the brain tumor microenvironment using the Lymph4D tool. • Transport metrics measured in different planes of view for a single tumor correlate. • There is high variability in interstitial velocity magnitude and diffusion coefficient slice to slice across a single tumor. We present a comprehensive methodology for measuring heterogeneous interstitial fluid flow in murine brain tumors using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) coupled with the computational tool, Lymph4D. This four-part protocol encompasses glioma cell preparation, tumor inoculation, MRI imaging protocol, and histological verification using Evans Blue. While conventional DCE-MRI analysis primarily focuses on vascular perfusion, our methods reveal untapped potential to extract crucial information about interstitial fluid dynamics, including directions, velocities, and diffusion coefficients. This methodology extends beyond glioma research, with applicability to conditions routinely imaged with DCE-MRI, thereby offering a versatile tool for investigating interstitial fluid dynamics across a wide range of diseases and conditions. Our methodology holds promise for accelerating discoveries and advancements in biomedical research, ultimately enhancing diagnostic and therapeutic strategies for a wide range of diseases and conditions. [ABSTRACT FROM AUTHOR]

Th9 cells, a subset of T-helper cells producing interleukin-9 (IL-9), play a vital role in the adaptive immune response and have diverse effects in different diseases. Regulated by transcription factors like PU.1 and IRF4, and cytokines such as IL-4 and TGF-β, Th9 cells drive tissue inflammation. This review focuses on their emerging role in immunopathophysiology. Th9 cells exhibit immune-mediated cancer cell destruction, showing promise in glioma and cervical cancer treatment. However, their role in breast and lung cancer is intricate, requiring a deeper understanding of pro- and anti-tumor aspects. Th9 cells, along with IL-9, foster T cell and immune cell proliferation, contributing to autoimmune disorders. They are implicated in psoriasis, atopic dermatitis, and infections. In allergic reactions and asthma, Th9 cells fuel pro-inflammatory responses. Targeting Foxo1 may regulate innate and adaptive immune responses, alleviating disease symptoms. This comprehensive review outlines Th9 cells' evolving immunopathophysiological role, emphasizing the necessity for further research to grasp their effects and potential therapeutic applications across diseases. PLAIN LANGUAGE SUMMARY: The immune system relies on CD4+ T cells, specifically Th9 cells, which produce Interleukin-9 (IL-9) to combat infections. Th9 cells have distinct functions regulated by various factors and are implicated in diseases, including cancer. Preclinical studies suggest Th9 cells could target tumors, but their role in cancer remains intricate. In lung and breast cancer, Th9 cells influence tumor growth and immune responses. Glioma research explores inducing Th9 cells to inhibit brain tumor growth. Th9 cells exhibit both positive and negative associations with colorectal cancer, lymphoma, and melanoma. Investigation into Th9 cells extends to autoimmune diseases like Graves' disease, inflammatory bowel disease, psoriasis, lupus, scleroderma, rheumatoid arthritis, and multiple sclerosis, where they may contribute to inflammation. In atopic dermatitis, elevated IL-9 levels correlate with disease severity, indicating Th9 cells' involvement in inflammation and cell activation. The complexity of Th9 cells underscores the necessity for disease-specific therapies. Understanding Th9 cells and IL-9 is pivotal for developing targeted treatments, emphasizing the nuanced role these cells play in diverse diseases and the potential for tailored therapeutic approaches. [ABSTRACT FROM AUTHOR]

Malignant brain tumours represent a significant medical challenge due to their aggressive nature and unpredictable locations. The growth of a brain tumour can result in a mass effect, causing compression and displacement of the surrounding healthy brain tissue and possibly leading to severe neurological complications. In this paper, we propose a multiphase mechanical model for brain tumour growth that quantifies deformations and solid stresses caused by the expanding tumour mass and incorporates anisotropic growth influenced by brain fibres. We employ a sharp interface model to simulate localised, non-invasive solid brain tumours, which are those responsible for substantial mechanical impact on the surrounding healthy tissue. By using patient-specific imaging data, we create realistic three-dimensional brain geometries and accurately represent ventricular shapes, to evaluate how the growing mass may compress and deform the cerebral ventricles. Another relevant feature of our model is the ability to simulate therapeutic protocols, facilitating the evaluation of treatment efficacy and guiding the development of personalized therapies for individual patients. Overall, our model allows to make a step towards a deeper analysis of the complex interactions between brain tumours and their environment, with a particular focus on the impact of a growing cancer on healthy tissue, ventricular compression, and therapeutic treatment. [ABSTRACT FROM AUTHOR]

To assess in patients with 1–10 brain metastases, each of which has been treated by neurosurgery or stereotactic radiosurgery, whether hippocampal sparing whole brain radiotherapy (HS-WBRT) better spares neurocognitive function (NCF) than standard WBRT. Further, to assess whether a phase III randomised trial of HS-WBRT would be feasible in the UK. A multicentre, randomised, open label phase II trial was undertaken, randomising patients to 30Gy in 10 fractions of WBRT or HS-WBRT. The primary endpoint was decline in Total recall using Hopkins Verbal Learning Test Revised (HVLT-R) at 4 months post treatment. To assess this, we aimed to recruit 84 patients over 3 years. Secondary endpoints included further measures of NCF, quality of life, duration of functional independence, local control of treated metastases, development of new metastases, disease control within the hippocampal regions, overall survival, steroid and antiepileptic medication requirements, and toxicity. The trial closed prematurely due to slower than anticipated recruitment. From April 2016 to January 2018, 23 patients were randomised. Follow up was a median of 25 months. Fifteen patients (6 WBRT, 9 HS-WBRT) were assessed for the primary endpoint; of these, 1 in each arm experienced significant decline in the 4-month HVLT-R Total recall score (p = 0.8). Patients in the HS-WBRT arm experienced less insomnia (p < 0.01) and drowsiness (p < 0.01). There were no differences in other secondary endpoints. A phase III randomised trial of HS-WBRT was shown not to be feasible at this time in the UK. As most randomised trials of HS-WBRT reported to date share common endpoints, including NCF, an individual patient data meta-analysis should be undertaken. • Changes in clinical practice away from whole brain radiotherapy necessitated early trial closure. • No significant differences in any neurocognitive outcomes were observed. • Compliance with serial neurocognitive function assessments was high. • Patients in the hippocampal sparing arm had significantly less insomnia and drowsiness. [ABSTRACT FROM AUTHOR]

Epithelioid glioblastoma (eGB) is a rare and aggressive brain tumor that primarily affects children and young adults. This article presents a case study of a young adult male with eGB, describing the characteristics of the tumor and the genetic alterations commonly found in eGB. The patient had a poor prognosis and died four months after diagnosis. The article emphasizes the importance of recognizing and studying this rare subtype of glioblastoma to improve patient care and prognosis. Treatment options for eGB include BRAF and MEK inhibitor combination therapy. [Extracted from the article]

The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model's ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model's decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

The treatment of brain tumors has been predominantly hindered by the limited ability of drugs to penetrate the blood-brain barrier. However, recent advancements in nanotechnology have presented a therapeutic potential by facilitating the delivery of drugs and macromolecules across the blood-brain barrier. These nanotechnology-enabled treatments target tumor cells, exploit tumor biology, enhance the pharmacokinetic properties of drugs, and minimize off-target side effects. Nanotechnology provides efficacy and safety of nano-enabled therapies for treating brain tumors. HIGHLIGHTS: Nanotechnology-enabled therapies offer potential for blood-brain barrier challenges. Nanotechnology-enabled therapies facilitate drug and macromolecule delivery to the brain. Nanotechnology enables targeted delivery to tumor cells, leveraging tumor biology. Nanotechnology improves pharmacokinetics and reduces off-target side-effects. Nanotechnology offers efficacy and safety of nano-enabled therapies for brain tumors. [ABSTRACT FROM AUTHOR]

Clinical images of brain tumors (BT) are crucial in the diagnostic process and contain substantial medical information. In neurosurgery and neurology, AI's application in retrieving and analyzing brain tumors leads to earlier, more accurate diagnoses and improves treatment planning. However, the accuracy of the existing methods for the physical retrieval of similar images needs to be improved. This paper introduces Gradient Ladybug Beetle Optimization-based LeNet (GLBO-LeNet) for the retrieval of brain tumor magnetic resonance images (MRI) from the medical datasets. This approach processes both input MRI images and query MRIs using the same pipeline. Tumor segmentation process is performed on these images using a 3D Convolutional Neural Network (CNN). Features are extracted from segmented images, incorporating a novel feature extraction method, LTDP based on Discrete Wavelet Transform (DWT) with Pyramid Histogram of Orientation (PHoG). The extracted features are utilized for tumor classification using LeNet-5, tuned by Gradient Ladybug Beetle Optimization (GLBO). The classified outputs from input MRI images are indexed in an image database. Similar images are retrieved and ranked using a proposed hybrid similarity measure, enabling efficient brain MRI image retrieval. In this study, the GLBO-LeNet-based brain tumor MRI retrieval system achieved an accuracy of 91.5%, a Prue-positive rate (TPR) of 91.9%, a True-negative rate (TNR) of 92.5%, a Positive predictive value (PPV) of 90.8% and a Negative predictive value (NPV) of 89.4%. [ABSTRACT FROM AUTHOR]

To improve the translation of preclinical cancer research data to successful clinical effect, there is an increasing focus on the use of primary patient‐derived cancer cells with limited growth in culture to reduce genetic and phenotype drift. However, these primary lines are less amenable to standardly used methods of exogenous DNA introduction. Adeno‐associated viral (AAV) vectors display tropism for a wide range of human tissues, avidly infect primary cells and have a good safety profile. In the present study, we therefore used a next‐generation sequencing (NGS) barcoded AAV screening method to assess transduction capability of a panel of 36 AAVs in primary cell lines representing high‐grade glioma (HGG) brain tumours including glioblastoma (GBM) and diffuse intrinsic pontine glioma (DIPG)/diffuse midline glioma (DMG). As proof of principle, we created a reporter construct to analyse activity of the transcriptional co‐activators yes‐associated protein (YAP) and transcriptional co‐activator with PDZ‐binding motif (TAZ). Transcriptional activation was monitored by promoter‐driven expression of the Timer fluorescent tag, a protein that fluoresces green immediately after transcription and transitions to red fluorescence over time. As expected, attempts to express the reporter in primary HGG cells from plasmid expression vectors were unsuccessful. Using the top candidate from the AAV screen, we demonstrate successful AAV‐mediated transduction of HGG cells with the YAP/TAZ dynamic activity reporter. In summary, the NGS‐screening approach facilitated screening of many potential AAVs, identifying vectors that can be used to study the biology of primary HGG cells. [ABSTRACT FROM AUTHOR]

The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE‐MRI data acquisition. Quantitative DCE‐MRI analysis was performed for computing various parameters such as wash‐out slope (Slope‐2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (Vp), and volume transfer constant (Ktrans). An approach based on data‐clustering algorithm, morphological operations, and quantitative DCE‐MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data‐clustering algorithms were evaluated on two types of quantitative maps: (a) Slope‐2, and (b) a new proposed combination of rCBV and Slope‐2 maps. Fluid‐attenuated inversion recovery‐MRI hyperintense lesions were also automatically segmented using deep learning‐based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE‐MRI parameters were obtained in the segmented tumor. t‐test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k‐means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high‐grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low‐grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and Vp maps show better visualization of tumor regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This case study reflects on the use of improvisation and songwriting to support a patient with significantly impaired short-term memory and long-term memory interference as a result of acquired brain injury. Memory has long been associated with personal identity, linking the past with the present, and enabling us to project into the future. This continuity of consciousness helps us to learn from, and make sense of our experiences, strengthening our internal representation of self. Disruption to short-term memory can significantly impact decision making, planning and initiation, all of which are key components of personal identity and self-expression. Supporting patient autonomy and self-expression through improvisation, and crafting lyrical content around personal preferences and events, sessions were designed to bolster his internalised sense of self through both revisiting old memories and facilitating new memory formation within the present. While short-term memory has been considered a conduit to long-term memory consolidation, and integral to the individual's self-expression, this case study implies short-term memory was neither the gatekeeper to formation of long-term memories, nor critical to maintaining a sense of self, and reflects on how music helped the client create and access new learning beyond procedural memories, anchoring the self in newly internalised self-expression. [ABSTRACT FROM AUTHOR]

Gliomas are primary brain tumors and are among the most malignant types. Adult‐type diffuse gliomas can be classified based on their histological and molecular signatures as IDH‐wildtype glioblastoma, IDH‐mutant astrocytoma, and IDH‐mutant and 1p/19q‐codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi‐scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse‐related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil‐mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction. [ABSTRACT FROM AUTHOR]

The MRE11, RAD50, and NBN genes encode the MRN complex sensing DNA breaks and directing their repair. While carriers of biallelic germline pathogenic variants (gPV) develop rare chromosomal instability syndromes, the cancer risk in heterozygotes remains controversial. We performed a systematic review and meta‐analysis of 53 studies in patients with different cancer diagnoses to better understand the cancer risk. We found an increased risk (odds ratio, 95% confidence interval) for gPV carriers in NBN for melanoma (7.14; 3.30–15.43), pancreatic cancer (4.03; 2.14–7.58), hematological tumors (3.42; 1.14–10.22), and prostate cancer (2.44, 1.84–3.24), but a low risk for breast cancer (1.29; 1.00–1.66) and an insignificant risk for ovarian cancer (1.53; 0.76–3.09). We found no increased breast cancer risk in carriers of gPV in RAD50 (0.93; 0.74–1.16; except of c.687del carriers) and MRE11 (0.87; 0.66–1.13). The secondary burden analysis compared the frequencies of gPV in MRN genes in patients from 150 studies with those in the gnomAD database. In NBN gPV carriers, this analysis additionally showed a high risk for brain tumors (5.06; 2.39–9.52), a low risk for colorectal (1.64; 1.26–2.10) and hepatobiliary (2.16; 1.02–4.06) cancers, and no risk for endometrial, and gastric cancer. The secondary burden analysis showed also a moderate risk for ovarian cancer (3.00; 1.27–6.08) in MRE11 gPV carriers, and no risk for ovarian and hepatobiliary cancers in RAD50 gPV carriers. These findings provide a robust clinical evidence of cancer risks to guide personalized clinical management in heterozygous carriers of gPV in the MRE11, RAD50, and NBN genes. [ABSTRACT FROM AUTHOR]

Introduction: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for improving therapies and patient outcomes. Methods: The current study comprehensively analyzed large-scale single-cell RNA sequencing and bulk RNA sequencing of glioma samples. By utilizing a series of advanced computational methods, this integrative approach identified the gene UPP1 (Uridine Phosphorylase 1) as a novel driver of glioma tumorigenesis and immune evasion. Results: High levels of UPP1 were linked to poor survival rates in patients. Functional experiments demonstrated that UPP1 promotes tumor cell proliferation and invasion and suppresses anti-tumor immune responses. Moreover, UPP1 was found to be an effective predictor of mutation patterns, drug response, immunotherapy effectiveness, and immune characteristics. Conclusions: These findings highlight the power of combining diverse machine learning methods to identify valuable clinical markers involved in glioma pathogenesis. Identifying UPP1 as a tumor growth and immune escape driver may be a promising therapeutic target for this devastating disease. [ABSTRACT FROM AUTHOR]

Background: Gliomas are the most common malignant brain tumors characterized by angiogenesis and invasive growth. A detailed understanding of its molecular characteristics could provide potential therapeutic targets. In the present study, we sought to explore the key gene CXCL8 in methionine metabolism in gliomas and its potential role in angiogenesis. Methods: U251 glioma cells were divided into control and methionine-restriction tolerant (constructed with 1/4 of the standard level of methionine in the culture medium) groups for transcriptome and metabolome analysis. To confirm the functions and mechanism of CXCL8 in glioma, heat map, volcano map, Go enrichment, gene set enrichment analysis (GSEA), protein–protein interaction network analysis, RT-PCR, western blotting assays, chicken embryo chorioallantoic membrane (CAM) test, chicken embryo yolk sac membrane (YSM) test and transplantation tumor nude mice model were performed. The TCGA database, CGGA database and clinical tissue samples were used to analyze CXCL8's significance on prognosis for patients with glioma. Results: CXCL8 expression was significantly up-regulated in methionine-restricted tolerance cells, it also activated vascular system development and triggered angiogenesis. CXCL8 expression is negatively correlated with survival prognosis in gliomas. Conclusions: Glioma cells promote angiogenesis in methionine-restricted environments through the activation of CXCL8, compensating for nutrient deprivation, and possibly contributing to the failure of antiangiogenic therapy. [ABSTRACT FROM AUTHOR]

Background: As the Internet becomes an increasingly vital source of medical information, the quality and reliability of brain tumor-related short videos on platforms such as TikTok and Bilibili have not been adequately evaluated. Therefore, this study aims to assess these aspects and explore the factors influencing the dissemination of such videos. Methods: A cross-sectional analysis was conducted on the top 100 brain tumorrelated short videos from TikTok and Bilibili. The videos were evaluated using the Global Quality Score and the DISCERN reliability instrument. An eXtreme Gradient Boosting algorithm was utilized to predict dissemination outcomes. The videos were also categorized by content type and uploader. Results: TikTok videos scored relatively higher on both the Global Quality Score (median 2, interquartile range [2, 3] on TikTok vs. median 2, interquartile range [1, 2] on Bilibili, p=1.51E-04) and the DISCERN reliability instrument (median 15, interquartile range [13, 18.25] on TikTok vs. 13.5, interquartile range [11, 16] on Bilibili, p=1.66E-04). Subgroup analysis revealed that videos uploaded by professional individuals and institutions had higher quality and reliability compared to those uploaded by non-professional entities. Videos focusing on disease knowledge exhibited the highest quality and reliability compared to other content types. The number of followers emerged as the most important variable in our dissemination prediction model. Conclusion: The overall quality and reliability of brain tumor-related short videos on TikTok and Bilibili were unsatisfactory and did not significantly influence video dissemination. Future research should expand the scope to better understand the factors driving the dissemination of medical-themed videos. [ABSTRACT FROM AUTHOR]

Objectives: Despite surgical resection, chemoradiation, and targeted therapy, brain tumors remain a leading cause of cancer-related death in children. Immunotherapy has shown some promise and is actively being investigated for treating childhood brain tumors. However, a critical step in advancing immunotherapy for these patients is to uncover targets that can be effectively translated into therapeutic interventions. Methods: In this study, our team performed a transcriptomic analysis across pediatric brain tumor types to identify potential targets for immunotherapy. Additionally, we assessed components that may impact patient response to immunotherapy, including the expression of genes essential for antigen processing and presentation, inhibitory ligands and receptors, interferon signature, and overall predicted T cell infiltration. Results: We observed distinct expression patterns across tumor types. These included elevated expression of antigen genes and antigen processing machinery in some tumor types while other tumors had elevated inhibitory checkpoint receptors, known to be associated with response to checkpoint inhibitor immunotherapy. Conclusion: These findings suggest that pediatric brain tumors exhibit distinct potential for specific immunotherapies. We believe our findings can guide investigators in their assessment of appropriate immunotherapy classes and targets in pediatric brain tumors. [ABSTRACT FROM AUTHOR]

Background and objective: Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long‐range relationships between voxels cannot be captured due to the localized character of the convolution procedure. Method: Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context‐aware module was introduced to capture the long‐range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. Result: Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state‐of‐the‐art methods. Conclusion: The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis. [ABSTRACT FROM AUTHOR]