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AbstractIntroductionMethodsResultsConclusionPaediatric brain tumours (PBT) are the most common cause of death among all childhood cancers. The neutrophil to lymphocyte ratio (NLR) has been shown to prognosticate many adult cancers. There is a paucity of literature on the NLR in PBTs. This study aims to study the link between PBTs and the NLR by comparing the preoperative serum NLR in children under 16 with brain tumours with their outcome in terms of grade of brain tumour and overall survival.This is a retrospective case control study. The NLRs were compared between patients with benign or malignant PBTs and patients who were alive or dead. Receiver-operating characteristic (ROC) curve analyses were performed and Youden indexes were calculated to evaluate the predictive potential of the NLR. A cut-off point of NLR > 4 was selected for the calculation of odds ratios.A total of 515 patients were included in this study. 53.8% were male. 66.2% had benign PBTs. 81.0% were alive at the time of the study. Patients with malignant PBTs had a higher NLR compared to patients with benign PBTs (p = 0.0066**). There was no difference in the NLR between patients who were dead compared to those who were alive (p = 0.1682 ns). The NLR had a Youden’s index of 0.1567 to predict malignant PBTs and 0.1285 to predict survival.A high NLR was associated with an increased odds of having a malignant PBT but a reliable cut-off point was not identified and the underlying mechanisms for this remain unknown. The NLR is a poor diagnostic biomarker due to its poor overall sensitivity and specificity. More research is required to further study the role of immunity in PBTs. [ABSTRACT FROM AUTHOR]

Glioblastoma is the most common primary malignant brain tumor in adults, with a median survival of just over 1 year. The failure of available treatments to achieve remission in patients with glioblastoma (GBM) has been attributed to the presence of cancer stem cells (CSCs), which are thought to play a central role in tumor development and progression and serve as a treatment‐resistant cell repository capable of driving tumor recurrence. In fact, the property of “stemness” itself may be responsible for treatment resistance. In this study, we identify a novel long noncoding RNA (lncRNA), cancer stem cell‐associated distal enhancer of SOX2 (CASCADES), that functions as an epigenetic regulator in glioma CSCs (GSCs). CASCADES is expressed in isocitrate dehydrogenase (IDH)‐wild‐type GBM and is significantly enriched in GSCs. Knockdown of CASCADES in GSCs results in differentiation towards a neuronal lineage in a cell‐ and cancer‐specific manner. Bioinformatics analysis reveals that CASCADES functions as a super‐enhancer‐associated lncRNA epigenetic regulator of SOX2. Our findings identify CASCADES as a critical regulator of stemness in GSCs that represents a novel epigenetic and therapeutic target for disrupting the CSC compartment in glioblastoma. [ABSTRACT FROM AUTHOR]

In the fifth edition of the World Health Organization's (WHO) classification of tumors of the central nervous system (CNS), molecular analysis is required for not only determining each tumor type but assessing its prognosis based on malignancy (CNS WHO grade). A notable example is the loss of tumor suppressor gene cyclin‐dependent kinase inhibitor 2A (CDKN2A), and CDKN2A homozygous deletion (HD) is a novel CNS WHO grade 4 marker in isocitrate dehydrogenase gene (IDH)‐mutant astrocytoma. However, incorporating molecular workup into the “routine diagnostics” of each brain tumor type remains a major challenge, especially in resource‐limited settings, including low‐ and middle‐income countries. We herein validated the usefulness of p16 and methylthioadenosine phosphorylase (MTAP) immunohistochemistry (IHC) as potential surrogates for the assessment of CDKN2A status in 20 IDH‐mutant astrocytoma cases. Of note, loss or retention of p16 and MTAP could accurately predict CDKN2A HD (p16: 87.5%, MTAP: 88.9%) or non‐HD (p16: 100%, MTAP: 100%) with a single marker alone. Importantly, we revealed contributing factors to gray‐zone IHC results (p16: 5–20%, MTAP: mosaic), including (1) hemizygous deletion of CDKN2A, (2) degenerative findings, and (3) intratumoral CDKN2A HD heterogeneity, the detailed histologic and molecular assessment of which would be a key to achieving integrated assessment of malignancy in IDH‐mutant astrocytoma. We characterized the pitfalls of each method and provided for the first time a practical flowchart of astrocytoma grading, contributing to a normalization of WHO2021‐based molecular diagnostics in resource‐limited settings. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic Resonance Elastography (MRE) allows the non-invasive quantification of tumor biomechanical properties in vivo. With increasing incidence of brain metastases, there is a notable absence of appropriate preclinical models to investigate their biomechanical characteristics. Therefore, the purpose of this work was to assess the biomechanical characteristics of B16 melanoma brain metastases (MBM) and compare it to murine GL261 glioblastoma (GBM) model using multifrequency MRE with tomoelastography post processing. Methods: Intracranial B16 MBM (n = 6) and GL261 GBM (n = 7) mouse models were used. Magnetic Resonance Imaging (MRI) was performed at set intervals after tumor implantation: 5, 7, 12, 14 days for MBM and 13 and 22 days for GBM. The investigations were performed using a 7T preclinical MRI with 20 mm head coil. The protocol consisted of single-shot spin echo-planar multifrequency MRE with tomoelastography post processing, contrast-enhanced T1- and T2- weighted imaging and diffusion-weighted imaging (DWI) with quantification of apparent diffusion coefficient of water (ADC). Elastography quantified shear wave speed (SWS), magnitude of complex MR signal (T2/T2*) and loss angle (j). Immunohistological investigations were performed to assess vascularization, blood-brain-barrier integrity and extent of glucosaminoglucan coverage. Results: Volumetric analyses displayed rapid growth of both tumor entities and softer tissue properties than healthy brain (healthy: 5.17 ± 0.48, MBM: 3.83 ± 0.55, GBM: 3.7 ± 0.23, [m/s]). SWS of MBM remained unchanged throughout tumor progression with decreased T2/T2* intensity and increased ADC on days 12 and 14 (p<0.0001 for both). Conversely, GBM presented reduced j values on day 22 (p=0.0237), with no significant alterations in ADC. Histological analysis revealed substantial vascularization and elevated glycosaminoglycan content in both tumor types compared to healthy contralateral brain. Discussion: Our results indicate that while both, MBM and GBM, exhibited softer properties compared to healthy brain, imaging and histological analysis revealed different underlying microstructural causes: hemorrhages in MBM and increased vascularization and glycosaminoglycan content in GBM, further corroborated by DWI and T2/T2* contrast. These findings underscore the complementary nature of MRE and its potential to enhance our understanding of tumor characteristics when used alongside established techniques. This comprehensive approach could lead to improved clinical outcomes and a deeper understanding of brain tumor pathophysiology. [ABSTRACT FROM AUTHOR]

Brain tumour is an abnormal growth of cells in the brain, and is a harmful and life-threatening disease worldwide. The rapid development of tumour cells increases the illness and death rates. Hence, the timely detection and classification of brain tumours is crucial for saving thousands of lives. However, the diagnosis of brain tumour is a challenging task because of different brain shapes, sizes and synaptic structures. In this research, the Linear Intensity Distribution Information (LIDI) and regularization parameter based Intuitionistic Fuzzy C-Means Algorithm (IFCM), namely LRIFCM is proposed for an effective segmentation of the brain portion that is affected by tumours. The different feature extraction approaches namely, LeNET, Gray-Level Co-occurrence matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract appropriate features from the brain images. Further, the Long Short-Term Memory (LSTM) classifier is used to classify the types of tumour based on the extracted features. Three different datasets namely, BRATS 2017, BRATS 2018 and Figshare brain datasets are used to analyse the proposed LRIFCM-LSTM method. The LRIFCM-LSTM is evaluated using both the segmentation and classification results. The segmentation using LRIFCM is assessed based on the parameters of SSIM, Jaccard, dice, accuracy and sensitivity, whereas the classification using LSTM is assessed based on accuracy, specificity, sensitivity, precision and F1-score. The existing researches: Hybrid-DANet, TECNN and VAE-GAN are used for comparison with the LRIFCM-LSTM method. The classification accuracy of LRIFCM-LSTM for BRATS 2018 dataset is 98.73 which is higher when compared to the TECNN. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of [68Ga]Ga-FAPI PET/CT in brain tumor imaging and to compare it with [18F]F-FDG PET/CT. Methods: 25 patients with MRI-suspected brain tumors were included in the study. They underwent whole body [18F]F-FDG PET/CT and [68Ga]Ga-FAPI PET/CT and brain scans. The target-to-background ratio (TBR) of brain tumors was calculated with the background of surrounding normal brain tissues uptake. The SUVmax and TBR of [18F]F-FDG PET/CT and [68Ga]Ga-FAPI PET/CT were compared. Additionally, the correlation between the uptake of the tracer by lesions with the greatest diameter of the lesion, the breadth of the oedema band, and the enhancement scores of the MRI enhancement scans was analyzed. Result: [68Ga]Ga-FAPI PET/CT was superior to [18F]F-FDG PET/CT for lesion detection, especially for brain metastases. Among gliomas, only high-grade gliomas uptake [68Ga]Ga-FAPI. Compared with [18F]F-FDG PET/CT, [68Ga]Ga-FAPI PET/CT had a lower SUVmax but a significantly better TBR. On [68Ga]Ga-FAPI PET/CT, the TBR may be associated with brain tumor blood-brain barrier disruption. Conclusions: [68Ga]Ga-FAPI PET/CT is a promising imaging tool for the assessment of brain tumors. Lack of physiological uptake of [68Ga]Ga-FAPI in normal brain parenchyma results in high TBR values, leading to better visualization of lesions and contributing to subsequent targeted therapy studies. [ABSTRACT FROM AUTHOR]

TTFields is a novel FDA-approved technology utilized for treating glioblastoma multiforme (GBM) within the brain. Presently, the effectiveness of therapy is evaluated through MRI imaging at random two-month intervals. Electrical impedance is an important and effective parameter for reflecting changes in tissue properties. In TTFields treatment for brain tumors, electrodes attached to the scalp deliver electric field energy to the tumor region. We hypothesize that these electrodes can also serve as sensors to detect impedance changes caused by tumor alterations in real time, thus continuously assessing the effectiveness of the treatment. In this work, we propose and scrutinize this hypothesis by conducting an in silico study to confirm the potential feasibility of the proposed concept. Our results indicate that the impedance amplitude change measured between opposing TTFields electrode arrays utilizing voltage and frequency of 50 V and 200 kHz (typical TTFields treatment parameters), has enough resolution (> 1mm) and Signal-to-Noise Ratio (> 40 dB) to evaluate tumor size change in the head. The impedance detection technique may be a significant augmentation to TTFields cancer treatment, enabling the continuous evaluation of safety and efficacy throughout the procedure. [ABSTRACT FROM AUTHOR]

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (<10 mm3) metastases detection and segmentation. We also demonstrate that the monthly rate of change of brain metastases over time are strongly predictive of overall survival (HR 1.27, 95%CI 1.18-1.38). We are releasing the dataset, codebase, and model weights for other cancer researchers to build upon these results and to serve as a public benchmark. Cancer is a dynamic disease, with one of its deadly complications being metastatic brain tumors. Here, the authors present a large, multimodal, longitudinal dataset of metastatic cancer, assembled from real world data for cancer research and artificial intelligence (AI) model development. They train time-dependent AI models, and find that novel, dynamic biomarkers exist that are predictive of systemic disease control and overall survival. [ABSTRACT FROM AUTHOR]

This article explores the use of convolutional neural networks (CNNs) in the detection and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). CNNs, which are adept at image recognition, can analyze brain imaging data to automatically identify relevant brain structures and abnormalities associated with ADHD. The integration of deep learning technology into ADHD diagnosis represents a significant advancement, allowing for more objective and data-driven approaches. The article also highlights specific studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using EEG signals and MRI scans. The authors suggest that future research should focus on integrating different types of data and developing personalized treatment plans while considering ethical considerations and explainable AI models. Overall, CNNs are seen as a promising tool in improving ADHD care and patient outcomes. [Extracted from the article]

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor among adults. Despite advancements in multimodality therapy for GBM, the overall prognosis remains poor, with an extremely high risk of recurrence. Currently, there is no established consensus on the optimal treatment option for recurrent GBM, which may include reoperation, reirradiation, chemotherapy, or a combination of the above. Bevacizumab is considered a first-line treatment option for recurrent GBM, as is temozolomide. However, in recurrent GBM, it is necessary to balance the risks and benefits of reirradiation in combination with bevacizumab and temozolomide. Herein, we report the case of a patient with recurrent GBM after standard treatment who benefited from stereotactic radiotherapy followed by bevacizumab and temozolomide maintenance therapy. Following 16 months of concurrent chemoradiotherapy (CCRT), the patient was diagnosed with recurrent GBM by a 3-T contrast-enhanced magnetic resonance imaging (MRI). The addition of localized radiotherapy to the ongoing treatment regimen of bevacizumab, in combination with temozolomide therapy, prolonged the patient's disease-free survival to over 2 years, achieving a significant long-term outcome, with no notable adverse effects observed. This clinical case may provide a promising new option for patients with recurrent GBM. [ABSTRACT FROM AUTHOR]

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state‐of‐the‐art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1‐score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Background: We sought to analyze, in well-defined clinical setting, the first 100 patients treated at the intraoperative MRI (iMRI) hybrid surgical theatre at our facility in a population-based setting to evaluate which pathologies are best approached with iMRI assisted surgeries, as this is not yet clearly defined. Methods: Patients undergoing surgery in the 3T iMRI hybrid surgical theatre at our neurosurgical department between December 2017 to May 2021 were included after informed consent. Demographic, clinical, surgical, histological, radiological and outcome parameters, as well as variables related to iMRI, were retrospectively collected and analyzed. Patients were subdivided into adult and pediatric cohorts. Results: Various neurosurgical procedures were performed; resection of tumors and epileptic foci, endoscopic skull base procedures including pituitary lesions, deep brain stimulation (DBS) and laser interstitial thermal therapy (LITT). In total, 41 patients were pediatric. An iMRI scan was carried out in 96% of cases and led to continuation of surgery in 50% of cases, mainly due to visualized remaining pathological tissue (95.2%). Median time to iMRI from intubation was 280 min and median total duration of surgery was 445 min. The majority of patients experienced no postoperative complications (70%), 13 patients suffered permanent postoperative deficits, predominantly visual. Conclusion: Herein, we demonstrate the first 100 patients undergoing neurosurgery aided by iMRI at our facility since introduction. Indications for surgery differed between pediatric and adult patients. The iMRI was utilized for tumor surgeries, particularly adult low-grade gliomas and pediatric tumors, as well as for epilepsy surgery and DBS. In this heterogenous population, iMRI led to continuation of surgery in 50%. To establish the benefit in maximizing the extent of resection in these brain pathologies future studies are recommended. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is an aggressive and highly malignant primary brain tumor characterized by rapid growth and a poor prognosis for patients. Despite advancements in treatment, the median survival time for GBM patients remains low. One of the crucial challenges in understanding and treating GBMs involves its remarkable cellular heterogeneity and adaptability. Central to the survival and proliferation of GBM cells is their ability to undergo metabolic reprogramming. Metabolic reprogramming is a process that allows cancer cells to alter their metabolism to meet the increased demands of rapid growth and to survive in the often oxygen- and nutrient-deficient tumor microenvironment. These changes in metabolism include the Warburg effect, alterations in several key metabolic pathways including glutamine metabolism, fatty acid synthesis, and the tricarboxylic acid (TCA) cycle, increased uptake and utilization of glutamine, and more. Despite the complexity and adaptability of GBM metabolism, a deeper understanding of its metabolic reprogramming offers hope for developing more effective therapeutic interventions against GBMs. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most common type of primary malignant brain tumor and has a dismal overall survival rate. To date, no GBM therapy has yielded successful results in survival for patients beyond baseline surgical resection, radiation, and chemotherapy. Immunotherapy has taken the oncology world by storm in recent years and there has been movement from researchers to implement the immunotherapy revolution into GBM treatment. Natural killer (NK) cell-based immunotherapies are a rising candidate to treat GBM from multiple therapeutic vantage points: monoclonal antibody therapy targeting tumor-associated antigens (TAAs), immune checkpoint inhibitors, CAR-NK cell therapy, Bi-specific killer cell engagers (BiKEs), and more. NK therapies often focus on tumor antigens for targeting. Here, we reviewed some common targets analyzed in the fight for GBM immunotherapy relevant to NK cells: EGFR, HER2, CD155, and IL-13Rα2. We further propose investigating the Lectin-like Transcript 1 (LLT1) and cell surface proliferating cell nuclear antigen (csPCNA) as targets for NK cell-based immunotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Although some of the novel systemic treatments, especially the group of tyrosine kinase inhibitors, have shown a durable central nervous system response, ionizing radiation remains the mainstay in the management of brain metastases (BM). Recent technological advancements have enabled the replacement of whole-brain radiotherapy with localized stereotactic radiotherapy (SRT) for treating up to 10 BM, either as a primary or combined treatment, reducing neurotoxicity and improving local control (LC). The delivered target dose and patient selection play a crucial role in enhancing treatment efficacy. However, there is still limited evidence supporting which factors most affect LC and which patients derive the greatest benefit from SRT. This retrospective single-institutional study evaluated treatment outcomes in a heterogeneous patient population treated with Linac-based SRT, with the aim of identifying potential dosimetric and clinical prognostic factors to better inform the decision-making process. Background/Objectives: To report on predictive factors in Linac-based SRT for single and multiple BM. Methods: Consecutive patients receiving either one or three fractions of single-isocenter coplanar VMAT SRT were retrospectively included. The GTV-PTV margin was 1–2 mm. The delivered target dose was estimated by recalculating the original plans on roto-translated CT according to errors recorded by post-treatment CBCT. The Kaplan–Meier method estimated local progression-free survival (LPFS), intracranial progression-free survival (IPFS), and overall survival (OS). Log-rank and Wilcoxon–Mann–Whitney tests evaluated inter-group differences, whereas Cox regression analysis assessed prognostic factors. Results: Fifty females and fifty males, with a median age of 69 years, received 107 SRTs. A total of 213 BM (range, 1–10 per treatment) with a median volume of 0.22 cc were irradiated with a median minimum BED of 59.5 Gy. The median delivered GTV D95 reduction was −0.3%. The median follow-up was 11 months. Nineteen LP events and a 1-year LC rate of 90.1% were observed. The GTV coverage did not correlate with LC, while the GTV volume was a risk factor for LP, with the 1-year rate dropping to 73% for volumes ≥ 0.88 cc. The median LPFS, IPFS, and OS were 6, 5, and 7 months, respectively. Multivariate analysis showed that patients with melanoma histology and those receiving a second or subsequent systemic therapy line had the worst outcomes, whereas patients with adenocarcinoma histology and mutations showed better results. Conclusions: The accuracy and efficacy of the Linac-based SRT approach for BM were confirmed, but the dose distribution alone failed to predict the treatment response, suggesting that other factors must be considered to maximize SRT outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Distinguishing radionecrosis from local recurrence after cranial radiotherapy remains a challenging diagnostic dilemma due to their overlapping clinical manifestations. Although new MRI techniques and metabolic imaging have been developed, diagnostic performance remains variable. Recently, our institution acquired a PET-MRI, enabling us to investigate the diagnostic performance of multiparametric MRI when used in dual-phase 18F-FDG PET-mpMRI to enhance diagnostic accuracy. Brain metastases or primary brain tumours had poor prognosis until the use of high dose radiotherapy. However, radionecrosis is a complex challenge in the post-radiotherapy management of these patients due to the difficulty of distinguishing this complication from local tumour recurrence. MRI alone has a variable specificity and sensibility, as does PET-CT imaging. We aimed to investigate the diagnostic performance of dual-phase 18F-FDG PET-mpMRI to distinguish cerebral radionecrosis from local tumour recurrence after cranial radiotherapy. A retrospective analysis was conducted between May 2021 and September 2022. Inclusion criteria encompassed patients with inconclusive MRI findings post-radiotherapy and history of cerebral radiotherapy for primary or metastatic brain lesions. Lesions are assessed qualitatively and semi-quantitatively. The gold standard to assess radionecrosis was histopathology or a composite criterion at three months. The study evaluated 24 lesions in 23 patients. Qualitative analysis yielded 85.7% sensitivity and 75% specificity. Semi-quantitative analysis, based on contralateral background noise, achieved 100% sensitivity and 50% specificity. Moreover, using contralateral frontal lobe background noise resulted in higher performances with 92% sensitivity and 63% specificity. Stratification by lesion type demonstrated 100% sensitivity and specificity rates for metastatic lesions. The diagnostic performance of dual-phase 18F-FDG PET-mpMRI shows promising results for metastatic lesions. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-small cell lung cancer (NSCLC) accounts for 80–85% of primary lung cancers. Radiation therapy is widely used to treat both primary NSCLC and lung cancer that has spread to the brain. However, radiotherapy responses are not durable and toxicity limits therapy. Therefore, there is an urgent need for new agents that can enhance the effectiveness of radiation therapy in lung cancer and reduce its toxicity. We find that AM-101, a benzodiazepine analog, enhances the effects of radiation and significantly improves the survival of mice with lung cancer brain-metastatic tumors. Additionally, AM-101 makes radiation treatment work better and slows down the growth of NSCLC subcutaneous xenograft tumors in mice. AM-101 activates the GABA(A) receptor in lung cancer cells, triggering selective autophagy by causing GABARAP to form multimers and stabilizing the mitochondrial receptor Nix. GABA(A) receptor activation may improve tumor control and allow for lower radiation doses, reducing toxicity. In non-small cell lung cancer (NSCLC) treatment, radiotherapy responses are not durable and toxicity limits therapy. We find that AM-101, a synthetic benzodiazepine activator of GABA(A) receptor, impairs the viability and clonogenicity of both primary and brain-metastatic NSCLC cells. Employing a human-relevant ex vivo 'chip', AM-101 is as efficacious as docetaxel, a chemotherapeutic used with radiotherapy for advanced-stage NSCLC. In vivo, AM-101 potentiates radiation, including conferring a significant survival benefit to mice bearing NSCLC intracranial tumors generated using a patient-derived metastatic line. GABA(A) receptor activation stimulates a selective-autophagic response via the multimerization of GABA(A) receptor-associated protein, GABARAP, the stabilization of mitochondrial receptor Nix, and the utilization of ubiquitin-binding protein p62. A high-affinity peptide disrupting Nix binding to GABARAP inhibits AM-101 cytotoxicity. This supports a model of GABA(A) receptor activation driving a GABARAP–Nix multimerization axis that triggers autophagy. In patients receiving radiotherapy, GABA(A) receptor activation may improve tumor control while allowing radiation dose de-intensification to reduce toxicity. [ABSTRACT FROM AUTHOR]

Simple Summary: Adaptive proton therapy (APT) is an evolving approach to proton beam scanning treatment planning. We performed dosimetric study on two groups of head and neck (H&N) patients to evaluate the influence of plan adaptation on planning target volume (PTV) and organs at risk (OARs) doses, resulting from the changes in patient anatomy observed in control computed tomography (CT). The adapted treatment plans, which incorporated the changes observed in the control CT images, statistically improved mostly PTV coverage compared to initial plan. Study shows that applying adaptive procedures in clinical workflow may increased efficiency by controlling the proper irradiation of the treated area for H&N cancer patients. Applying a proton beam in radiotherapy enables precise irradiation of the tumor volume, but only for continuous assessment of changes in patient anatomy. Proton beam range uncertainties in the treatment process may originate not only from physical beam properties but also from patient-specific factors such as tumor shrinkage, edema formation and sinus filling, which are not incorporated in tumor volume safety margins. In this paper, we evaluated variations in dose distribution in proton therapy resulting from the differences observed in the control tomographic images and the dosimetric influence of applied adaptive treatment. The data from weekly computed tomography (CT) control scans of 21 patients, which serve as the basis for adaptive radiotherapy, were used for this study. Dosimetric analysis of adaptive proton therapy (APT) was performed on patients with head and neck (H&N) area tumors who were divided into two groups: patients with tumors in the sinus/nasal area and patients with tumors in the brain area. For this analysis, the reference treatment plans were forward-calculated using weekly control CT scans. A comparative evaluation of organ at risk (OAR) dose-volume histogram (DVH) parameters, as well as conformity and homogeneity indices, was conducted between the initial and recalculated dose distributions to assess the necessity of the adaptation process in terms of dosimetric parameters. Changes in PTV volume after replanning were observed in seventeen patient cases, showing a discrepancy of over 1 cm 3 in ten cases. In these cases, tumor progression occurred in 30% of patients, while regression was observed in 70%. The statistical analysis indicates that the use of the adaptive planning procedure results in a statistically significant improvement in dose distribution, particularly in the PTV area. The findings led to the conclusion that the adaptive procedure provides significant advantages in terms of dose distribution within the treated volume. However, when considering the entire patient group, APT did not result in a statistically significant dose reduction in OARs (α = 0.05). [ABSTRACT FROM AUTHOR]

Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5–20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12–18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest to mimic the actual human head. The reflection coefficients can be recorded and analyzed instantaneously, reducing high electromagnetic radiation consumption. It has been shown that a single-band detection point is not adequate to classify the nonlinear tumor and head model parameters. On the other hand, dual-band and tri-band metamaterial-inspired antennas, with additional detecting points, create a continuous function solution for the nonlinear problem by adding extra observation points using multiple-band excitation. The point mapping values can be used to enhance the tumor detection capability. Two-point mapping showed a consistent trend between the S11 value order and the tumor size, while three-point mapping can also be used to demonstrate the correlation between the S11 value order and the tumor size. This proposed multi-detection point technique can be applied to a sensor for other nonlinear property targets. Moreover, a set of antennas with different polarizations, orientations, and arrangements in a network could help to obtain the highest sensitivity and accuracy of the whole system. [ABSTRACT FROM AUTHOR]

The common octopus (Octopus vulgaris) is an excellent candidate for aquaculture diversification, due to its biological traits and high market demand. To ensure a high-quality product while maintaining welfare in captive environments, it is crucial to develop non-invasive methods for testing health biomarkers. Proteins found in skin mucus offer a non-invasive approach to monitoring octopus welfare. This study compares the protein profiles in the skin mucus of wild, aquarium-maintained, and senescent specimens to identify welfare biomarkers. A tandem mass tag (TMT) coupled with an Orbitrap Eclipse Tribrid mass spectrometer was used to create a reference dataset from octopus skin mucus, identifying 1496 non-redundant protein groups. Although similar profiles were observed, differences in relative abundances led to the identification of potential biomarkers, including caspase-3-like, protocadherin 4, deleted in malignant brain tumors, thioredoxin, papilin, annexin, cofilin and mucin-4 proteins. Some of these proteins also revealed potential as bioactive peptides. This investigation provides the most extensive analysis of the skin mucus proteome in the common octopus and is the first to explore how aquarium maintenance and senescence alter the mucus proteome. This research highlights the potential of skin mucus protein/peptides as non-invasive monitoring biomarkers in cultured animals. [ABSTRACT FROM AUTHOR]

Like most tumors, glioblastoma multiforme (GBM), the deadliest brain tumor in human adulthood, releases extracellular vesicles (EVs). Their content, reflecting that of the tumor of origin, can be donated to nearby and distant cells which, by acquiring it, become more aggressive. Therefore, the study of EV-transported molecules has become very important. Particular attention has been paid to EV proteins to uncover new GBM biomarkers and potential druggable targets. Proteomic studies have mainly been performed by "bottom-up" mass spectrometry (MS) analysis of EVs isolated by different procedures from conditioned media of cultured GBM cells and biological fluids from GBM patients. Although a great number of dysregulated proteins have been identified, the translation of these findings into clinics remains elusive, probably due to multiple factors, including the lack of standardized procedures for isolation/characterization of EVs and analysis of their proteome. Thus, it is time to change research strategies by adopting, in addition to harmonized EV selection techniques, different MS methods aimed at identifying selected tumoral protein mutations and/or isoforms due to post-translational modifications, which more deeply influence the tumor behavior. Hopefully, these data integrated with those from other "omics" disciplines will lead to the discovery of druggable pathways for novel GBM therapies. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is one of the most prevalent cancerous brain tumors. Former studies have reported that exosomes derived from M1‐polarized macrophages (M1 exosomes) inhibit tumor occurrence and development through delivery of tumor suppressor genes. Also, microRNA‐142‐3p (miR‐142‐3p) has been verified to function as a tumor suppressor. GBM cell proliferation was evaluated by Cell Counting Kit‐8 (CCK‐8), colony formation assay and 5‐ethynyl‐2′‐deoxyuridine (EdU) assay; cell apoptosis was determined by flow cytometry analysis and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Mechanism investigations were conducted for analyzing the molecular mechanism by which miR‐142‐3p and M1 exosomes affect GBM progression. Upregulation of miR‐142‐3p expression was detected in M1‐polarized macrophages and M1 exosomes. M1 exosomes inhibit GBM cell proliferation and trigger cell apoptosis. Functionally, miR‐142‐3p silencing promotes the proliferation and inhibits the apoptosis of GBM cells treated with M1 exosomes. As for molecular mechanism, miR‐142‐3p inhibits GBM cell growth via targeting high‐mobility group box 1 (HMGB1). In addition, miR‐142‐3p/HMGB1 axis affects GBM cell immune escape through modulation of programmed death‐1/programmed death ligand‐1 (PD‐1/PD‐L1) checkpoint. Our study demonstrated that exosomal miR‐142‐3p from M1‐polarized macrophages suppresses cell growth and immune escape in GBM through regulating HMGB1‐mediated PD‐1/PD‐L1 checkpoint. [ABSTRACT FROM AUTHOR]

This research presents a novel deep learning framework for MRI-based brain tumour (BT) detection. The input brain MRI image is first acquired from the dataset. Once the images have been obtained, they are passed to an image preprocessing step where a median filter is used to eliminate noise and artefacts from the input image. The tumour-tumour region segmentation module receives the denoised image and it uses RP-Net to segment the BT region. Following that, in order to prevent overfitting, image augmentation is carried out utilizing methods including rotating, flipping, shifting, and colour augmentation. Later, the augmented image is forwarded to the feature extraction phase, wherein features like GLCM and proposed EGDP formulated by including entropy with GDP are extracted. Finally, based on the extracted features, BT detection is accomplished based on the proposed deep convolutional belief network (DCvB-Net), which is formulated using the deep convolutional neural network and deep belief network.The devised DCvB-Net for BT detection is investigated for its performance concerning true negative rate, accuracy, and true positive rate is established to have acquired values of 93%, 92.3%, and 93.1% correspondingly. [ABSTRACT FROM AUTHOR]

Background/Objectives: Brain tumor patients confront numerous challenges arising from diagnosis and treatment, and these impact the patient's physical, mental, and social functions at all levels. Primary informal caregivers assume a pivotal role in home-based patient care. Of particular importance are the coping strategies employed by family caregivers, as they can influence both their own health and the overall quality of home care. This study aimed to explore the associations among family function, caregiving competence, and coping strategies among primary informal caregivers. Methods: This study adopted a cross-sectional correlational design and convenience sampling to survey the primary informal caregivers of 111 brain tumor patients. The study instruments included the Family Assessment Device General Function, Caregiving Competence Scale, and Revised Ways of Coping Checklist. Results: The findings of this study revealed a significant positive correlation between the family function of primary informal caregivers and their employment of emotion-focused coping (r = 0.209, p < 0.05). Furthermore, caregiving competence exhibited a positive association with problem-focused coping (r = 0.242, p < 0.05) and emerged as a significant predictor of problem-focused coping (β = 0.182, p < 0.05). However, neither family function (r = 0.059, p < 0.05) nor caregiving competence (r = 0.031, p < 0.05) demonstrated significant associations with total coping strategies. Conclusions: The findings of this study affirmed that enhancing the caregiving competence of primary informal caregivers of brain tumor patients can facilitate the adoption of problem-focused coping strategies. [ABSTRACT FROM AUTHOR]

Positronium is abundantly produced within the molecular voids of a patient's body during positron emission tomography (PET). Its properties dynamically respond to the submolecular architecture of the tissue and the partial pressure of oxygen. Current PET systems record only two annihilation photons and cannot provide information about the positronium lifetime. This study presents the in vivo images of positronium lifetime in a human, for a patient with a glioblastoma brain tumor, by using the dedicated Jagiellonian PET system enabling simultaneous detection of annihilation photons and prompt gamma emitted by a radionuclide. The prompt gamma provides information on the time of positronium formation. The photons from positronium annihilation are used to reconstruct the place and time of its decay. In the presented case study, the determined positron and positronium lifetimes in glioblastoma cells are shorter than those in salivary glands and those in healthy brain tissues, indicating that positronium imaging could be used to diagnose disease in vivo. [ABSTRACT FROM AUTHOR]

Background: This study assessed the cost-effectiveness of proton beam therapy (PBT) compared to conventional radiotherapy (CRT) for treating patients with brain tumors in Sweden. Methods: Data from a longitudinal non-randomized study performed between 2015 and 2020 was used, and included adult patients with brain tumors, followed during treatment and through a one-year follow-up. Clinical and demographic data were sourced from the longitudinal study and linked to Swedish national registers to get information on healthcare resource use. A cost-utility framework was used to evaluate the cost-effectiveness of PBT vs. CRT. Patients in PBT group (n = 310) were matched with patients in CRT group (n = 40) on relevant observables using propensity score matching with replacement. Costs were estimated from a healthcare perspective and included costs related to inpatient and specialized outpatient care, and prescribed medications. The health outcome was quality-adjusted life-years (QALYs), derived from the EORTC-QLQ-C30. Generalized linear models (GLM) and two-part models were used to estimate differences in costs and QALYs. Results: PBT yielded higher total costs, 14,639 US$, than CRT, 13,308 US$, with a difference of 1,372 US$ (95% CI, -4,914–7,659) over a 58 weeks' time horizon. Further, PBT resulted in non-significantly lower QALYs, 0.746 compared to CRT, 0.774, with a difference of -0.049 (95% CI, -0.195–0.097). The probability of PBT being cost-effective was < 30% at any willingness to pay. Conclusions: These results suggest that PBT cannot be considered a cost-effective treatment for brain tumours, compared to CRT. Trial registration: Not applicable. Novelty and Impact: This study informs on the cost-effectiveness of proton beam therapy (PBT) versus conventional radiotherapy (CRT) for patients with brain tumors in Sweden. Amidst global concerns regarding the cost-effectiveness of PBT, our investigation fills a notable gap by providing evidence from the Swedish healthcare landscape. Using real-world data, our study demonstrates the application of established health economic methodology to compare PBT and CRT for brain tumors, furthering research in this area. [ABSTRACT FROM AUTHOR]

Glioma is a primary brain tumor that grows quickly, has an unfavorable prognosis, and can spread intracerebrally. Glioma cells rely on glucose as the major energy source, and glycolysis plays a critical role in tumorigenesis and progression. Substrate utilization shifts throughout glioma progression to facilitate energy generation and biomass accumulation. This metabolic reprogramming promotes glioma cell proliferation and metastasis and ultimately decreases the efficacy of conventional treatments. Non-coding RNAs (ncRNAs) are involved in several glucose metabolism pathways during tumor initiation and progression. These RNAs influence cell viability and glucose metabolism by modulating the expression of key genes of the glycolytic pathway. They can directly or indirectly affect glycolysis in glioma cells by influencing the transcription and post-transcriptional regulation of oncogenes and suppressor genes. In this review, we discussed the role of ncRNAs in the metabolic reprogramming of glioma cells and tumor microenvironments and their abnormal expression in the glucometabolic pathway in glioma. In addition, we consolidated the existing theoretical knowledge to facilitate the use of this emerging class of biomarkers as biological indicators and potential therapeutic targets for glioma. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common brain tumor. IDH mutations occur frequently in glioma, indicating a more favorable prognosis. We aimed to explore energy metabolism-related genes in glioma to promote the research and treatment. Methods: Datasets were obtained from TCGA and GEO databases. Candidate genes were screened by differential gene expression analysis, then functional enrichment analysis was conducted on the candidate genes. PPI was also carried out to help determine the target gene. GSEA and DO analysis were conducted in the different expression level groups of the target gene. Survival analysis and immune cell infiltrating analysis were performed as well. Results: We screened 34 candidate genes and selected GLUD1 as the target gene. All candidate genes were significantly enriched in 10 KEGG pathways and 330 GO terms. GLUD1 expression was higher in IDH-mutant samples than IDH-wildtype samples, and higher in normal samples than tumor samples. Low GLUD1 expression was related to poor prognosis according to survival analysis. Most types of immune cells were negatively related to GLUD1 expression, but monocytes and activated mast cells exhibited significantly positive correlation with GLUD1 expression. GLUD1 expression was significantly related to 119 drugs and 6 immune checkpoint genes. GLUD1 was able to serve as an independent prognostic indicator of IDH-mutant glioma. Conclusion: In this study, we identified an energy metabolism-related gene GLUD1 potentially contributing to favorable clinical outcomes of IDH-mutant glioma. In glioma, GLUD1 related clinical outcomes and immune landscape were clearer, and more valuable information was provided for immunotherapy. [ABSTRACT FROM AUTHOR]

Background: The Multidisciplinary Tumor Board (MTB) is a collaborative platform involving specialists in oncology, surgery, radiology, pathology, and radiotherapy, and aims to optimize diagnostics and treatments. Despite MTB's widespread benefits, limited literature addresses its application in pediatric neuro-oncology. After a literature revision on pediatric neuro-oncology MTB, our study describes our institute's pediatric neuro-oncology MTB, focuses on evaluating its impact and the neuroradiologist's role in patient-centric approaches, considering recent genetic insights into pediatric brain tumors. Materials and methods: Literature Review concerning pediatric neuro-oncology MTB from January 2002 to June 2024. Clinical Data: retrospective study of all patient files presented in the pediatric neuro-oncology MTB (pnMTB) between 2019 and 2022. Statistical analysis was mainly carried out by directly comparing the absolute or relative values of the respective parameters examined; qualitative variables compared mainly with the chi-square test, quantitative variables mainly with the t-test. Results: Literature Review: 7 papers encompass a multidisciplinary approach for the pediatric CNS tumors. Clinical data: A total of 236 discussions were analyzed representing 107 patients. Median age was 14,3 years (range: 6 months – 17 years). The requests for case evaluations primarily came from the pediatric oncologists (83%) and neurosurgeons (14.8%), and they were mainly addressed to the neuroradiologists (70.3%). Proposals during pnMTB mainly involved imaging follow-up (47.8%) and management with chemotherapy (34.7%). Changes in patient treatment (CPT) occurred in 115 cases, and pediatric neuroradiologist intervention contributed to 72.4% of these changes. Conclusion: Thanks to their multidisciplinarity, high number of cases discussed, and usual respect for their proposals, the pnMTB has made it possible to improve the coordination among specialties involved in patient management, to apply the recent protocols, and to exchange knowledge among teams managing pediatric CNS tumors. [ABSTRACT FROM AUTHOR]

The diverse roles of the low‐density lipoprotein receptor family (LDLR) have been associated with many processes critical to maintaining central nervous system (CNS) health and contributing to neurological diseases or cancer. In this review, we provide a comprehensive understanding of the LDLR's involvement in common brain tumors, specifically high‐grade gliomas, emphasizing the receptors' critical role in the pathophysiology and progression of these tumors due to LDLR's high expression. We delve into LDLR's role in regulating cellular uptake and transport through the brain barrier. Additionally, we highlight LDLR's role in activating several signaling pathways related to tumor proliferation, migration, and invasion, engaging readers with an in‐depth understanding of the molecular mechanisms at play. By synthesizing current research findings, this review underscores the significance of LDLR during tumorigenesis and explores its potential as a therapeutic target for high‐grade gliomas. The collective insights presented here contribute to a deeper appreciation of LDLR's multifaceted roles and implications for physiological and pathological states, opening new avenues for tumor treatment. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor and known for its challenging prognosis. Sonodynamic therapy (SDT) is an innovative therapeutic approach that shows promise in tumor elimination by activating sonosensitizers with low‐intensity ultrasound. In this study, a novel sonosensitizer is synthesized using Cu‐doped carbon dots (Cu‐CDs) for the sonodynamic treatment of GBM. Doping with copper transforms the carbon dots into a p–n type semiconductor having a bandgap of 1.58 eV, a prolonged lifespan of 10.7 µs, and an improved electron‐ and hole‐separation efficiency. The sonodynamic effect is efficiency enhanced. Western blot analysis reveals that the Cu‐CDs induces a biological response leading to cell death, termed as cuproptosis. Specifically, Cu‐CDs upregulate dihydrosulfanyl transacetylase expression, thereby establishing a synergistic therapeutic effect against tumor cell death when combined with SDT. Furthermore, Cu‐CDs exhibit excellent permeability through the blood‐brain barrier and potent anti‐tumor activity. Importantly, the Cu‐CDs effectively impede the growth of glioblastoma tumors and prolong the survival of mice bearing these tumors. This study provides support for the application of carbon‐based nanomaterials as sonosensitizers in tumor therapy. [ABSTRACT FROM AUTHOR]

This article presents a case report on the cytopathological findings of granular cell glioblastoma (GCBG) in intraoperative squash smear preparations. GCBG is a rare and aggressive type of brain tumor that is difficult to diagnose. The article describes the microscopic features of GCBG, including the presence of large macrophage-like tumor cells with eosinophilic granular cytoplasm and distinct nucleoli. The authors emphasize the importance of caution in diagnosing GCBG and suggest communicating potential differential diagnoses to neurosurgeons. [Extracted from the article]

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. [ABSTRACT FROM AUTHOR]

Microbubble-enhanced ultrasound provides a noninvasive physical method to locally overcome major obstacles to the accumulation of blood-borne therapeutics in the brain, posed by the blood-brain barrier (BBB). However, due to the highly nonlinear and coupled behavior of microbubble dynamics in brain vessels, the impact of microbubble resonant effects on BBB signaling and function remains undefined. Here, combined theoretical and prospective experimental investigations reveal that microbubble resonant effects in brain capillaries can control the enrichment of inflammatory pathways that are sensitive to wall shear stress and promote differential expression of a range of transcripts in the BBB, supporting the notion that microbubble dynamics exerted mechanical stress can be used to establish molecular, in addition to spatial, therapeutic windows to target brain diseases. Consistent with these findings, a robust increase in cytotoxic T-cell accumulation in brain tumors was observed, demonstrating the functional relevance and potential clinical significance of the observed immuno-mechano-biological responses. The impact of ultrasound-controlled microbubble dynamics on the blood-brain barrier remains largely unexplored. Through theoretical and experimental research, the authors show that microbubble resonant effects in brain vessels can control the enrichment of inflammatory pathways and modulate cytotoxic T-cell infiltration in tumours. [ABSTRACT FROM AUTHOR]

Purpose: Studies have demonstrated that hormonal imbalance, such as elevated level of estrogen or reduced level of progesterone, was the main inducing factor of uterine leiomyoma (UL) development and some cancers. UL has been reported to be associated with several cancers in observational studies. However, the causal associations between UL and cancers remain unclear. Methods: A two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal associations between UL and 16 site-specific cancers using the public databases. Four methods, namely, the inverse variance weighting (IVW), MR-Egger, weighted median, and weighted mode, were applied in our MR analysis. Sensitivity tests were also performed to evaluate the robustness of these causal associations. Results: The IVW analysis indicated that genetically predicted UL increased the risk of low malignant potential ovarian cancer [odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.06-1.40, p = 0.004], serous ovarian cancer (OR = 1.29, 95% CI: 1.10-1.52, p = 0.002), invasive mucinous ovarian cancer (OR = 1.24, 95% CI: 1.08-1.44, p = 0.003), clear cell ovarian cancer (OR = 1.25, 95% CI: 1.03-1.51, p = 0.023), breast cancer (OR = 1.07, 95% CI: 1.02-1.11, p = 0.002), and brain tumor (OR = 1.23, 95% CI: 1.06-1.42, p = 0.007). Conversely, genetically predicted UL reduced the risk of gastric cancer (OR = 0.91, 95% CI: 0.85-0.98, p = 0.008). The causal effects were consistent in the sensitivity analysis. Conclusions: Our results demonstrated that UL exhibits a causal relationship with high risk of several cancers. We suggest reinforcing the cancer screening in UL patients to enable the early detection of cancers. [ABSTRACT FROM AUTHOR]

This document discusses the role of LOXL2 in promoting the occurrence and progression of glioma, a type of brain tumor. LOXL2 activates autophagy, a cellular process that promotes tumor growth and resistance to chemotherapy. The specific mechanisms of LOXL2's relationship with histological grade and autophagy's regulation of epithelial-mesenchymal transition (EMT) are still unclear and require further study. The findings suggest that targeting LOXL2 in combination with chemotherapy may be an effective treatment strategy for glioma and potentially other tumors. [Extracted from the article]

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems. Observation of glioblastoma invasion in subcortical brain regions is challenging. Here the authors develop a Deep3P workflow to enable longitudinal deep imaging of glioblastoma and its microenvironment by combining three-photon microscopy and deep learning-based analysis, unraveling the invasion routes of these tumor cells into and within the white matter. [ABSTRACT FROM AUTHOR]

Background: Craniotomy to remove brain tumors is an intricate procedure with multiple postoperative symptoms. However, there has been limited research on the symptom networks of these patients. To this end, this study aims to explore these symptom networks, revealing their interplay to inform better symptom control, hasten the discovery of postoperative issues, and tailor Enhanced Recovery After Surgery (ERAS) protocols, all to enhance recovery and enhance patient care. Methods: From September 2023 to March 2024, 211 patients with primary brain tumors who underwent craniotomy at Shanghai Tongji Hospital were recruited. Their symptoms were assessed using the MDASI-BT (M.D. Anderson Symptom Inventory Brain Tumor Module) one day post-craniotomy. The symptom network of 22 symptoms was visualized using R, with central and bridge symptoms identified. Results: Sadness (rs=2.482) and difficulty in understanding (rs=1.138) have the highest strength of all symptoms, indicating they are the central symptoms. Sadness (rb=2.155) and loss of appetite (rb=1.828) have the highest value of betweenness, indicating they are the bridge symptoms. Strong correlations were found between difficulty in understanding and difficulty in speaking (r = 0.701), distress and sadness (r = 0.666), fatigue and lethargy (r = 0.632), and nausea and vomiting (r = 0.601). Subgroup analysis revealed that noninvasive tumor patients exhibited similar symptom networks to the overall cohort, whereas invasive tumor patients showed weak symptom connections, resulting in no discernible network. Conclusion: This study underscores the importance of understanding symptom networks in brain tumor patients post-craniotomy, highlighting key symptom interrelationships. These insights can guide more effective symptom management, early complication detection, and optimization of ERAS protocols, ultimately enhancing recovery and patient care. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and MethodsResultsConclusionsMeningiomas are the most common type of primary brain tumour. Hyperostosis is commonly associated but remains incompletely understood. This study aimed to evaluate the relationship between meningioma-associated hyperostosis and other tumour variables.We retrospectively analysed 245 patients with 263 cranial meningiomas (202 CNS WHO grade 1, 53 grade 2, and 8 grade 3) who underwent surgery over a three-year period. Meningiomas adjacent to the skull were included. Demographic, radiological, and tumour characteristics were analysed using standard statistical methods.Hyperostosis was evident in 99 (38%) of meningiomas. The most common subtypes were meningothelial, transitional, fibrous, atypical, and anaplastic. There were no statistically significant relationships between hyperostosis and bone invasion, and CNS WHO grade and histological subtype. Hyperostosis was more common in skull base meningiomas than in convexity meningiomas (p = 0.001). Ki-67 index was significantly related to CNS WHO grade but not histological subtype when grade was considered. Mean Ki-67 index was higher in meningiomas without hyperostosis (p = 0.03). There was no such relationship with bone invasion (p = 0.29). Univariate and multivariate analysis revealed that Ki-67 index was negatively correlated with hyperostosis (p = 0.03), while bone invasion (p < 0.001) and skull base location (p = 0.03) were positively correlated with hyperostosis.Hyperostosis did not appear to be related to CNS WHO grade or histological subtype. Proliferative activity appeared to be higher in meningiomas without hyperostosis and hyperostosis was associated with evidence of bone invasion and skull base location. [ABSTRACT FROM AUTHOR]

Gliomas are highly malignant brain tumours that remain refractory to treatment. Treatment is typically surgical intervention followed by concomitant temozolomide and radiotherapy; however patient prognosis remains poor. Voltage gated ion channels have emerged as novel targets in cancer therapy and inhibition of a potassium selective subtype (hERG, Kv11.1) has demonstrated antitumour activity. Unfortunately blockade of hERG has been limited by cardiotoxicity, however hERG channel agonists have produced similar chemotherapeutic benefit without significant side effects. In this study, electrophysiological recordings suggest the presence of hERG channels in the anaplastic astrocytoma cell line SMA-560, and treatment with the hERG channel agonist NS1643, resulted in a significant reduction in the proliferation of SMA-560 cells. In addition, NS1643 treatment also resulted in a reduction of the secretion of matrix metalloproteinase-9 and SMA-560 cell migration. When combined with temozolomide, an additive impact was observed, suggesting that NS1643 may be a suitable adjuvant to temozolomide and limit the invasiveness of glioma. [ABSTRACT FROM AUTHOR]

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various sizes and shapes, making their manual detection from magnetic resonance imaging (MRI) scans a subjective and challenging task for healthcare professionals, hence necessitating automated solutions. This study investigates the potential of deep learning, specifically the DenseNet architecture, to automate brain tumor classification, aiming to enhance accuracy and generalizability for clinical applications. We utilized the Figshare brain tumor dataset, comprising 3,064 T1-weighted contrast-enhanced MRI images from 233 patients with three prevalent tumor types: meningioma, glioma, and pituitary tumor. Four pre-trained deep learning models—ResNet, EfficientNet, MobileNet, and DenseNet—were evaluated using transfer learning from ImageNet. DenseNet achieved the highest test set accuracy of 96%, outperforming ResNet (91%), EfficientNet (91%), and MobileNet (93%). Therefore, we focused on improving the performance of the DenseNet, while considering it as base model. To enhance the generalizability of the base DenseNet model, we implemented a fine-tuning approach with regularization techniques, including data augmentation, dropout, batch normalization, and global average pooling, coupled with hyperparameter optimization. This enhanced DenseNet model achieved an accuracy of 97.1%. Our findings demonstrate the effectiveness of DenseNet with transfer learning and fine-tuning for brain tumor classification, highlighting its potential to improve diagnostic accuracy and reliability in clinical settings. [ABSTRACT FROM AUTHOR]

Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach-the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors inMRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field. [ABSTRACT FROM AUTHOR]

Gene‐editing technology shows great potential in glioblastoma (GBM) therapy. Due to the complexity of GBM pathogenesis, a single gene‐editing‐based therapy is unlikely to be successful; therefore, a multi‐gene knockout strategy is preferred for effective GBM inhibition. Here, a non‐invasive, biodegradable brain‐targeted CRISPR/Cas12a nanocapsule is used that simultaneously targeted dual oncogenes, EGFR and PLK1, for effective GBM therapy. This cargo nanoencapsulation technology enables the CRISPR/Cas12a system to achieve extended blood half‐life, efficient blood‐brain barrier (BBB) penetration, active tumor targeting, and selective release. In U87MG cells, the combinatorial gene editing system resulted in 61% and 33% knockout of EGFR and PLK1, respectively. Following systemic administration, the CRISPR/Cas12a system demonstrated promising brain tumor accumulation that led to extensive EGFR and PLK1 gene editing in both U87MG and patient‐derived GSC xenograft mouse models with negligible off‐target gene editing detected through NGS. Additionally, CRISPR/Cas12a nanocapsules that concurrently targeted the EGFR and PLK1 oncogenes showed superior tumor growth suppression and significantly improved the median survival time relative to nanocapsules containing single oncogene knockouts, signifying the potency of the multi‐oncogene targeting strategy. The findings indicate that utilization of the CRISPR/Cas12a combinatorial gene editing technique presents a practical option for gene therapy in GBM. [ABSTRACT FROM AUTHOR]

Objectives: The radiographic assessment of the head is a crucial part of headache care. A computed tomography (CT) scan enables a more detailed analysis of the condition and more focused care. This study examined head CT scans to determine what kinds of anomalies were present in patients with headaches as their primary complaint. Methods: We evaluated 4 years' worth of CT scan data from head exams conducted at two diagnostic facilities in Ghana's western and central regions. We examined data on 477 patients with a headache as their primary complaint between January 2017 and December 2020. We employed chi‐square and Fisher's exact tests (where applicable) to compare head CT diagnoses between age groups, gender, headache subtypes, and brain lesion subgroups. Results: There were 53.5% (n = 255) females and 46.5% (n = 222) males in the study. The average age of patients was 38.67 ± 17.23 years, with an annual rate of abnormal CT diagnoses ranging from 35.9% in 2017 to 45.4% in 2022. Abnormal head CT diagnoses are strongly correlated with age groups and patient gender (p = 0.011 and p = 0.009, respectively). Of the 202 patients, 15.3% and 24.3% were classified as intracranial lesions and extracranial lesions, respectively. Maxillary sinusitis affected nearly 60% of the patients, while tumors and hemorrhages affected 25.2% and 11.9%, respectively. Conclusions: A CT scan of the head is essential to detect abnormalities in nearly 50% of patients suffering from various degrees of headache. Sinusitis, brain tumors, and hemorrhage were common lesions detected. It is crucial to create local standard operating procedures to promote better utilization of this type of imaging service, particularly among patients who have been diagnosed with headaches. [ABSTRACT FROM AUTHOR]

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f 1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f 1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT. [ABSTRACT FROM AUTHOR]

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information. [ABSTRACT FROM AUTHOR]

Objective: Treatment of pediatric brain tumors is associated with potential long‐term cognitive sequelae. Patients treated with craniospinal irradiation for posterior fossa tumors are at high risk. New biomarkers that could help to differentiate treatment effects from other causes of cognitive dysfunction would be valuable in tailoring optimal survivorship care. Biomarkers that reflect biological mechanisms behind treatment‐associated cognitive decline would also be important in the evaluation of future treatment regimens for pediatric brain or skull base tumors. Methods: In this biomarker‐finding study, 10 adult survivors of pediatric medulloblastoma, skull base tumors, and posterior fossa low‐grade glioma underwent study specific lumbar puncture at a minimum of 17 years following treatment. We analyzed cerebrospinal fluid biomarkers reflecting neuron and astrocyte integrity, amyloid metabolism, inflammation, extracellular matrix, synaptic integrity, and blood–brain barrier function. The values were compared with biomarker levels in healthy controls of comparable age. Results: Biomarkers reflecting neuronal injury (neurofilament light chain protein), astrocyte injury or activation (glial fibrillary acidic protein) as well as inflammation (YKL‐40) were significantly elevated in cancer survivors compared to controls. Biomarkers reflecting amyloid metabolism showed a pattern of decrease in patients treated for medulloblastoma. Interpretation: The results suggest a potential chronic low‐grade neurodegeneration and astrocyte activation in patients treated for pediatric brain or skull base tumors. Protein biomarkers of CNS disease could potentially be used to increase our understanding of the contribution from different tumor treatments with regard to long‐term symptoms in cancer patients. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma is one of the most malignant brain tumors, which is also known as primary neuroepithelial tumors. Glioblastoma malignant tumors include 20-40% of brain tumors. The LMO1 gene is located at position 11P15.4 and is introduced as an oncogene in some cancers. This study was conducted for the first time in Iran to identify and investigate the relationship between LMO1 gene mutations and glioblastoma. Materials and Methods: In this research, the Touchdown PCR technique and DNA sequencing method were used in 35 blood samples of people with glioblastoma multiforme and 40 control samples. Bioinformatics analyses were also performed to investigate the pathogenic effect of nucleotide changes in this gene. Results: In this study, four point mutations were identified, of which two of the new mutations were misense and led to amino acid changes in one of the important domains of the protein (p.M135K and p.N148H), which indicates their potential pathogenicity. Our results of bioinformatics databases predicted that both of these mutations affect protein function, such that they can disrupt this domain and impair its function. Also, a nucleotide change was observed in the 3'UTR region of this gene (c.*74A>G), which is located at the binding site of two regulatory miRNAs and is expected to disrupt the binding of these miRNAs to the target sequence. Conclusion: These findings predict that any mutations in the LIM-sensitive domains in the LMO1 gene are significantly related to the pathogenesis of glioblastoma and most likely have a significant impact on the function of this transcriptional cofactor. [ABSTRACT FROM AUTHOR]

Cell phone use is increasing and now includes nearly 6.9 billion subscribers. A common concern is the effect of long-lasting phone calls on the formation of brain tumors, due to the proximity of this region. The aim of the following review was to verify this association along with a potential molecular background. The results of epidemiological studies are inconclusive. Most of them do not indicate a significantly increased risk of central nervous system cancers in phone users. However, some indicate that there is an increased risk of gliomas and a worse prognosis for patients with long-term phone use (in terms of cumulative hours and number of calls). Experimental studies show that radiation emitted by phones is able to induce changes in cell biology by generating oxidative stress, causing DNA damage and affecting gene expression. Therefore, further observation of the population and evaluation of the results of ongoing studies is needed to accurately assess this risk. [ABSTRACT FROM AUTHOR]

Background. Colloid cyst treatment with purely endoscopic surgery is considered to be safe and effective. Complete capsule removal for gross total resection is usually recommended to prevent recurrence but may not always be safely feasible. Our objective was to go for complete endoscopic surgery using mainly aspiration, manipulation and coagulation with complete capsule resection and discuss the rationale for the procedure. Methods and materials. A case report with a third ventricle colloid cyst was surgically treated with a complete endoscopic excision using the proper technique. Results. Our patient underwent Transforaminal endoscopic surgery and the cyst was excised completely and the capsule was removed intentionally. Cyst remnants were absent on postoperative MRI. Mild Intraventricular haemorrhage was an intraoperative complication. Surgery was statistically associated with cyst volume and ventricular size reduction. There were no serious complications postoperatively. Follow-up did not show any recurrence or remnant growth that needed further treatment. Conclusion. Gross total resection may be the main objective for selected cases and seems to be safer while preserving good results, especially in a limited resource environment. Surgical planning allows the surgeon to choose among the different resection routes and techniques available. Decisions are predominantly based on preoperative imaging and intraoperative findings. Full-endoscopic approach for third ventricle colloid cyst removal is a feasible technique. Cyst aspiration followed by grasping and rotational manoeuvre for the cyst wall provides total removal with the resolution of the obstruction if present and relief of symptoms. [ABSTRACT FROM AUTHOR]