Treatment Of Brain Tumors Research Articles

Sonodynamic therapy for adult-type diffuse gliomas: past, present, and future.

Intra-axial brain tumors persist as significant clinical challenges. Aggressive surgical resection carries risk of morbidity, and the blood-brain barrier (BBB) prevents optimal pharmacological interventions. There is a clear clinical demand for innovative and less invasive therapeutic strategies for patients, especially those that can augment established treatment protocols. Focused ultrasound (FUS) has emerged as a promising approach to manage brain tumors. Sonodynamic therapy (SDT), a subset of FUS, utilizes sonosensitizers activated by ultrasound waves to generate reactive oxygen species (ROS) and induce tumor cell death. This review explores the historical evolution and rationale behind SDT, focusing on its mechanisms of action and potential applications in brain tumor management. A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Preclinical studies have demonstrated the efficacy of various sonosensitizers, including 5-aminolevulinic acid (5-ALA), fluorescein, porphyrin derivatives, and nanoparticles, in conjunction with FUS for targeted tumor therapy and BBB disruption. Clinical trials have shown promising results in terms of safety and efficacy, although further research is needed to fully understand the potential adverse effects and optimize treatment protocols. Challenges such as skull thickness affecting FUS penetration, and the kinetics of BBB opening require careful consideration for the successful implementation of SDT in clinical practice. Future directions include comparative studies of different sonosensitizers, optimization of FUS parameters, and exploration of SDT's immunomodulatory effects. SDT represents a promising frontier in the treatment of aggressive brain tumors, offering hope for improved patient outcomes.

Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring.

The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm3. Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation.

A review of sonodynamic therapy for brain tumors.

Sonodynamic therapy (SDT) is gaining attention as a promising new noninvasive brain tumor treatment that targets and selectively kills tumor cells, with limited side effects. This review examines the mechanisms of SDT and ongoing clinical trials looking at optimization of sonication parameters for potential treatment of glioblastoma (GBM) and diffuse intrinsic pontine glioma (DIPG). The results in the first patient with recurrent GBM treated at the Mayo Clinic are briefly discussed. The authors of this literature review used electronic databases including PubMed, EMBASE, and OVID. Articles reporting relevant preclinical and clinical trials were identified by searching for text words/phrases and MeSH terms, including the following: "sonodynamic therapy," "SDT," "focused ultrasound," "5-ALA," "ALA," "brain tumors," "diffuse pontine glioma," "glioblastoma," and "high grade glioma." Preclinical and clinical trials investigating the specific use of SDT in brain tumors were reviewed. In preclinical models of high-grade glioma and GBM, SDT has shown evidence of targeted tumor cell death via the production of reactive oxygen species. Emerging clinical trial results within recurrent GBM and DIPG show evidence of successful treatment response, with minimal side effects experienced by recruited patients. So far, SDT has been shown to be a promising noninvasive cancer treatment that is well tolerated by patients. The authors present pilot data suggesting good radiological response of GBM to a single SDT treatment, with unpublished observation of a lack of off-target effects even after multiple (monthly) sonication outpatient treatments. The scope of the clinical trials of SDT is to investigate whether it can be the means by which the fatal diagnosis of GBM or DIPG is converted into that of a chronic, treatable disease. SDT is safe, repeatable, and better tolerated than both chemotherapy and radiotherapy. It has been shown to have an effect in human cancer therapy, but more clinical trials are needed to establish standardized protocols for sonosensitizer delivery, treatment parameters, and combination therapies. The most appropriate timing of treatment also remains to be determined-whether to prevent recurrence in the postoperative period, or as a salvage option in patients with recurrent GBM for which redo surgery is inappropriate. It is hoped that SDT will also be developed for a wider spectrum of clinical indications, such as metastases, meningioma, and low-grade glioma. Further clinical trials are in preparation.

CEP-1347 Boosts Chk2-Mediated p53 Activation by Ionizing Radiation to Inhibit the Growth of Malignant Brain Tumor Cells.

Radiation therapy continues to be the cornerstone treatment for malignant brain tumors, the majority of which express wild-type p53. Therefore, the identification of drugs that promote the ionizing radiation (IR)-induced activation of p53 is expected to increase the efficacy of radiation therapy for these tumors. The growth inhibitory effects of CEP-1347, a known inhibitor of MDM4 expression, on malignant brain tumor cell lines expressing wild-type p53 were examined, alone or in combination with IR, by dye exclusion and/or colony formation assays. The effects of CEP-1347 on the p53 pathway, alone or in combination with IR, were examined by RT-PCR and Western blot analyses. The combination of CEP-1347 and IR activated p53 in malignant brain tumor cells and inhibited their growth more effectively than either alone. Mechanistically, CEP-1347 and IR each reduced MDM4 expression, while their combination did not result in further decreases. CEP-1347 promoted IR-induced Chk2 phosphorylation and increased p53 expression in concert with IR in a Chk2-dependent manner. The present results show, for the first time, that CEP-1347 is capable of promoting Chk2-mediated p53 activation by IR in addition to inhibiting the expression of MDM4 and, thus, CEP-1347 has potential as a radiosensitizer for malignant brain tumors expressing wild-type p53.

2274: Demyelination in white matter after pediatric brain tumor treatment, assessed with quantitative MRI

2274: Demyelination in white matter after pediatric brain tumor treatment, assessed with quantitative MRI

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ. Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4). Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437cm- 1, while a negative correlation was found with the intensity of the protein band at 1260cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83). Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

Is maximum tolerated dose (MTD) chemotherapy scheduling optimal for glioblastoma multiforme?

In this study, we investigate a control problem involving a reaction–diffusion partial differential equation (PDE). Specifically, the focus is on optimizing the chemotherapy scheduling for brain tumor treatment to minimize the remaining tumor cells post-chemotherapy. Our findings establish that a bang-bang increasing function is the unique solution, affirming the MTD scheduling as the optimal chemotherapy profile. Several numerical experiments on a real brain image with parameters from clinics are conducted for tumors located in the frontal lobe, temporal lobe, or occipital lobe. They confirm our theoretical results and suggest a correlation between the proliferation rate of the tumor and the effectiveness of the optimal treatment.

Comprehensive insight into 3D bioprinting technology for brain tumor modeling

Glioblastoma is one of the most common primary malignant brain tumors with a poor prognosis and high mortality rate. Because only small lipophilic molecules can penetrate the blood-brain barrier, chemotherapy and immunotherapy are limited in their ability to effectively treat brain tumor. Three-dimensional (3D) bioprinting has the potential to model the 3D environment of human pathology and diseases directly. In many cancers, 3D bioprinting technology closely mimics the tumor microenvironment, making it a promising tool for drug screening and uncovering the mechanisms of cancer initiation and progression. Recent 3D bioprinting technologies have been developed to recreate the dynamic interactions between the tumor and endothelial cells. Brain tumor models using 3D bioprinting technology can reconstruct the biophysical heterogeneity and immune interactions in the brain. These advanced models regulate the organization of tumor structures for preclinical drug testing and reveal the immunological pathways involved in brain tumor. Here, we highlight 3D bioprinting technologies that can replace conventional in vitro models for brain tumor treatment.

MR-guided laser interstitial thermal therapy in the treatment of brain tumors and epilepsy.

MR-guided Laser Interstitial Thermal Therapy (MRgLITT) is a minimally invasive neurosurgical technique increasingly used for the treatment of drug-resistant epilepsy and brain tumors. Utilizing near-infrared light energy delivery guided by real-time MRI thermometry, MRgLITT enables precise ablation of targeted brain tissues, resulting in limited corridor-related morbidity and expedited postoperative recovery. Since receiving CE marking in 2018, the adoption of MRgLITT has expanded to more than 40 neurosurgical centers across Europe. In epilepsy treatment, MRgLITT can be applied to various types of focal lesional epilepsy, including mesial temporal lobe epilepsy, hypothalamic hamartoma, focal cortical dysplasias, periventricular heterotopias, cavernous malformations, dysembryoplastic neuroepithelial tumors (DNET), low-grade gliomas, tuberous sclerosis, and in disconnective surgeries. In neuro-oncology, MRgLITT is used for treating newly diagnosed and recurrent primary brain tumors, brain metastases, and radiation necrosis. This comprehensive review presents an overview of the current evidence and technical considerations for the use of MRgLITT in treating various pathologies associated with drug-resistant epilepsy and brain tumors.

Selection for proton radiotherapy of grade 1–3 glioma patients

BackgroundFor adult patients with grade 1–3 gliomas, identifying patients with an indication for proton therapy (PT) can be challenging due to sparse evidence supporting its benefits. In this study, we aimed to ensure national consensus and develop a decision support tool to aid clinicians in identifying patients with grade 1–3 gliomas eligible for PT. MethodsSixty-one historic patients referred for postoperative radiotherapy for glioma grade 1–3 were included in this study and had new photon therapy and PT plans calculated. These plans along with clinical parameters were presented to neurooncologists with experience in treating brain tumours. The patients were presented at three workshops (WSs), where each neurooncologist individually had to choose between photon and proton therapy. Important parameters were selected using cross validation. Multivariable logistic regression was used to predict the neurooncologists’ treatment modality choice. ResultsAt the three WSs 23, 24 and 19 randomly selected patients were presented. Seventy-five percent of the neurooncologists agreed for 14 patients (61%), 16 patients (67%) and 15 patients (79%) at WS1, WS2 and WS3. Age at radiotherapy and difference in mean dose (ΔDmean) to the residual brain were significant predictors of the choice of treatment modality, p < 0.001. Model coefficients were: βage = 0.07 per year (95% confidence interval [CI] = 0.05–0.09), and βΔdose = -0.27 per Gy (95% CI=-0.36--0.18). ConclusionHigher degree of agreement was reached. Age and ΔDmean to the residual brain significantly predicted the choice of radiation modality. We have developed a decision support model which may aid in the selection of patients with glioma grade 1–3 to PT.

Trochlear nerve schwannoma with concomitant osimertinib-responsive stage IV lung adenocarcinoma: illustrative case.

The prognosis for cancer patients has been improved because of the development of molecularly targeted drugs. Treatment of intracranial tumors must be personalized while prioritizing the treatment of comorbid cancers. A 38-year-old man presented with bloody sputum, bilateral multiple nodules, and a mass in the lower lobe of his right lung. Bronchoscopy revealed stage IV lung adenocarcinoma with an epidermal growth factor receptor (EGFR) mutation. Screening head magnetic resonance imaging revealed a 38-mm-diameter mass in the left petroclival area. Because the patient was neurologically intact, the treatment of lung adenocarcinoma was prioritized, and the third-generation EGFR-tyrosine kinase inhibitor osimertinib was used. Although nodules in the lung began to shrink, the intracranial lesion expanded and caused hydrocephalus, necessitating a ventriculoperitoneal shunt. The tumor also caused diplopia, dysarthria, and gait abnormalities. A left anterior transpetrosal approach was used to remove the tumor derived from the trochlear nerve. The pathological examination revealed schwannoma. Neurological symptoms improved following surgery. Osimertinib was continued during the perioperative period. Osimertinib was effective for lung adenocarcinoma but not for trochlear nerve schwannoma, which required surgical intervention. It is necessary to tailor the treatment of benign brain tumors in patients with concurrent malignant cancers. https://thejns.org/doi/10.3171/CASE24144.

Potential for boron neutron capture therapy (BNCT) with ASCT2-targeted boron agents.

233 Background: Boron Neutron Capture Therapy (BNCT) is a cutting-edge particle irradiation technique that utilizes the nuclear reaction triggered by the irradiation of non-radioactive boron-10 with thermal neutrons, selectively annihilating tumor cells that have incorporated boron. In the clinical arena, Boronophenylalanine (BPA), a phenylalanine compound targeting amino acid receptors, has been the cornerstone of BNCT. While BNCT has been effective against malignant gliomas, its dependency on BPA uptake has illuminated the existence of inherent resistance within specific cells, tissues, and cancer types. This study pivots towards the Alanine-serine-cysteine transporter 2 (ASCT2)—a transporter distinct from LAT1—investigating its potential as a gateway for the development of innovative boron carriers, with the aim of expanding the clinical applicability and effectiveness of BNCT. Methods: This research embarked on in vitro investigations to evaluate boron accumulation in F98 and C6 rat glioma cells, and 9L rat gliosarcoma, following 24 hours of exposure to BPA and GluB-2 (10 μg B/ml each). Complementary in vivo studies involved the intravenous administration of these compounds to an F98 rat brain tumor model, with the subsequent measurement of boron distribution at 2.5, 6, and 24 hours post-administration. The efficacy of these treatments was then ascertained through neutron irradiation experiments, gauging therapeutic outcomes via the survival periods of the subjects. Results: In vitro findings highlighted that GluB-2 facilitated a significantly higher intracellular boron concentration compared to BPA in F98 cells (p=0.02, Student's t-test). In vivo results indicated that the apex of boron biodistribution in tumors was achieved at 2.5 hours post-BPA treatment and at 6 hours post-GluB-2 treatment, with GluB-2 securing a superior maximum intratumor boron concentration. Survival analysis revealed a mean survival of 28.0±2.5 days for the neutron alone group, 37.7±5.0 days for the group receiving BPA IV + BNCT at 2.5 hours, 55.1±19.9 days for the group treated with GluB-2 IV + BNCT at 6 hours, and 25.3±1.4 days for the untreated group. Remarkably, the group treated with GluB-2 demonstrated significantly prolonged survival compared to the BPA-treated group (p&lt;0.001, log-rank test). Conclusions: Combining BNCT with GluB-2 showed superior efficacy over BPA. Further research is needed to optimize boron concentration timing and ASCT2 expression levels, yet GluB-2 presents a promising advancement in BNCT, potentially transforming malignant brain tumor treatment.

Comparative Study of Denoising and Segmentation Techniques for Accurate Brain Tumor Detection in MRI

Background: Brain tumor incidence is on the rise each year, with more than 130 identified types. Precise segmentation models play a vital role in the diagnosis and treatment of brain tumors. This study specifically investigates the utilization of diffusion-based denoising techniques and thresholding methods for segmenting brain tumors from MRI images. Objective: The objective of this study is to examine and compare the efficacy of the Perona-malik and Weickert diffusion techniques in denoising brain MRI images. Additionally, the study aims to assess their performance in threshold-based segmentation of brain tumors. Moreover, it also aims to evaluate the compatibility, benefits, and limitations of the Perona-Malik and Weickert diffusion methods in the denoising of brain MRI images and the effect of denoising on segmentation. Methods: In this study, the Perona-Malik and Weickert diffusion methods are employed to denoise brain MRI images. The denoised images are then subjected to thresholding using both binary and fuzzy approaches, utilizing a triangular membership function. The performance of the diffusion techniques is evaluated using metrics, such as Mean Square Error and Peak Signal to Noise Ratio. Additionally, segmentation models are assessed using metrics such as Dice Similarity Coefficient, Jaccard Similarity Coefficient, and Structural Similarity Measurement Index. Results: The Perona-Malik and Weickert diffusion methods exhibit compatibility with various types of noise, each having its own set of advantages and limitations. The Weickert diffusion method excels in preserving image structure and texture during thresholding. Conclusion: The study provides evidence for the effectiveness of diffusion-based denoising techniques in segmenting brain tumors from MRI images. Specifically, the Weickert diffusion method outperforms in preserving essential image characteristics during thresholding. Additionally, fuzzy thresholding proves to be more successful in accurately segmenting brain tumors. These findings contribute to the advancement of precise models for brain tumor segmentation, ultimately enhancing the diagnosis and treatment of these tumors.

Revealing the link between gut microbiota and brain tumor risk: a new perspective from Mendelian randomization.

Recent studies have shown that gut microbiota may be related to the occurrence of brain tumors, but direct evidence is lacking. This study used the Mendelian randomization study (MR) method to explore the potential causal link between gut microbiota and brain tumors. We analyzed the genome-wide association data between 211 gut microbiota taxa and brain tumors, using the largest existing gut microbiota Genome-Wide Association Studies meta-analysis data (n=13266) and combining it with brain tumor data in the IEU OpenGWAS database. We use inverse-variance weighted analysis, supplemented by methods such as Mendelian randomization-Egger regression, weighted median estimator, simple mode, and weighted mode, to assess causality. In addition, we also conducted the Mendelian randomization-Egger intercept test, Cochran's Q test, and Mendelian randomization Steiger directionality test to ensure the accuracy of the analysis. Quality control includes sensitivity analysis, horizontal gene pleiotropy test, heterogeneity test, and MR Steiger directionality test. Our study found that specific gut microbial taxa, such as order Lactobacillales and family Clostridiaceae1, were positively correlated with the occurrence of brain tumors, while genus Defluviitaleaceae UCG011 and genus Flavonifractor were negatively correlated with the occurrence of brain tumors. The Mendelian randomization-Egger intercept test showed that our analysis was not affected by pleiotropy (P>0.05). This study reveals for the first time the potential causal relationship between gut microbiota and brain tumors, providing a new perspective for the prevention and treatment of early brain tumors. These findings may help develop new clinical intervention strategies and point the way for future research.

Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review.

The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge. Deep learning (DL) is very convenient for extracting relevant information from large amounts of data that has increased in medical history and imaging records, which shortens diagnosis time, that would otherwise overwhelm manual methods. In addition, DL aids in automated tumor segmentation, classification, and diagnosis. DL models such as the Brain Tumor Classification Model and the Inception-Resnet V2, or hybrid techniques that enhance these functions and combine DL networks with support vector machine and k-nearest neighbors, identify tumor phenotypes and brain metastases, allowing real-time decision-making and enhancing preoperative planning. AI algorithms and DL development facilitate radiological diagnostics such as computed tomography, positron emission tomography scans, and magnetic resonance imaging (MRI) by integrating two-dimensional and three-dimensional MRI using DenseNet and 3D convolutional neural network architectures, which enable precise tumor delineation. DL offers benefits in neuro-interventional procedures, and the shift toward computer-assisted interventions acknowledges the need for more accurate and efficient image analysis methods. Further research is needed to realize the potential impact of DL in improving these outcomes.

Treatment Reproducibility in Brain Stereotactic Radiotherapy Using a Shim Mask Versus a Standard Mask.

Introduction This study aimed to evaluate the setup accuracy of the new shim mask with mouth bite compared to the standard full brain mask in stereotactic radiosurgery (SRS) and radiotherapy (SRT) treatments for brain metastases or tumors. Method A combined retrospective and prospective design was employed, involving 40 patients treated at our center. Patients previously treated using standard head masks formed the retrospective cohort, while those treated with the Shim mask and mouth bite formed the prospective cohort. Daily cone-beam computed tomography (CBCT) scans were obtained before each treatment session to ensure patient setup accuracy. Key metrics included absolute shifts in translational and rotational directions, the number of repeat CBCTs, and the time interval between CBCTs. Results The Shim mask significantly reduced the mean setup errors in the lateral translation (p=0.022) from 0.17 cm (SD=0.10) to 0.10 cm (SD=0.10), and in X-axis rotation (p=0.030) from 0.79° (SD=0.43) to 0.47° (SD=0.47). By considering cutoff points of 1 mm in translational and 1° in rotational directions, the Shim mask was significantly more accurate in the lateral direction (p=0.004). Moreover, while 70% of patients in the standard group required repeat CBCT scans, none in the Shim group did, resulting in an average time saving of 10.4 minutes per patient. Conclusion The Shim mask with mouth bite offers enhanced immobilization accuracy in SRT/SRS treatments, leading to time and potential cost savings by reducing the need for repeat CBCT scans. This underscores the importance of adopting innovative immobilization techniques to optimize patient outcomes.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Charting the success of neuronavigation in brain tumor surgery: from inception to adoption and evolution.

Neuronavigation, explored as an intra-operative adjunct for brain tumor surgery three decades ago, has become globally utilized with a promising upward trajectory. This study aims to chart its success from idea to adoption and evolution within the US and globally. A three-pronged methodology included a systematic literature search, impact analysis using NIH relative citation ratio (RCR) and Altmetric scores, and assessment of patent holdings. Data was dichotomized for US and international contexts. The first neuronavigation publication stemmed from Finland in 1993, marking its inception. Over three decades, the cumulative number of 323 studies, along with the significantly increasing publication trend (r = 0.74, p < 0.05) and distribution across 34 countries, underscored its progressive and global adoption. Neuronavigation, mostly optical systems (58%), was utilized in over 19,000 cases, predominantly for brain tumor surgery (84%). Literature impact showed a robust cumulative median RCR score surpassing that for NIH-funded studies (1.37 vs. 1.0), with US studies having a significantly higher median RCR than international (1.71 vs. 1.21, p < 0.05). Technological evolution was characterized by adjuncts, including micro/exo/endoscope (21%), MRI (17%), ultrasound (10%), and CT (7%). Patent analysis demonstrated academic and industrial representation with an interdisciplinary convergence of medical and computational sciences. Since its inception thirty years ago, neuronavigation has been adopted worldwide, and it has evolved with adjunct technology integration to enhance its meaningful use. The current neuronavigation innovation pipeline is progressing, with academic and industry partnering to advance its further application in treating brain tumor patients.

OPTIMIZED U-NET SEGMENTATION MODEL AND DEEP MAXOUT CLASSIFIER FOR BRAIN TUMOR CLASSIFICATION

The most serious nervous system ailment, a brain tumor impairs one’s health seriously and ultimately results in death. MRI, one of the most frequently used medical imaging modalities for brain tumors, has emerged as the main diagnostic system for the treatment and study of brain tumors. It was challenging to segment and classify the many kinds of brain tumors. The swarm intelligence approach has the potential to more efficiently and effectively tackle a number of issues. Therefore, this work develops a novel model for the classification of brain tumors that includes various phases. Primarily, the input image is preprocessed via the proposed median filtering that aids in removing noises. Subsequently, segmentation is done via optimal U-Net. For precise segmentation, the weights are tuned optimally by battle royale optimization with the Bernoulli randomization (BROBR) algorithm. Then, features like the proposed local Gabor XOR pattern (PLGXP), texton features, gray level co-occurrence matrix (GLCM), and correlation features are extracted. Finally, BTC is done using a deep maxout network (DMO) that provides the final output on the absence or presence of a brain tumor.

CSF biomarkers of neurotoxicity in childhood cancer survivors after cranial radiotherapy or surgery.

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. 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. 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. 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.