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.

OBJECTIVE: Brain tumors, particularly glioblastoma multiforme (GBM), present significant prognostic challenges despite multimodal therapies, including surgical resection, chemotherapy, and radiotherapy. One major obstacle is the limited drug delivery across the blood-brain barrier (BBB). Focused ultrasound (FUS) combined with systemically administered microbubbles has emerged as a non-invasive, targeted, and reversible approach to transiently open the BBB, thus enhancing drug delivery. This review examines clinical trials employing BBB opening techniques to optimise pharmacotherapy for brain tumors, evaluates current challenges, and proposes directions for further research.
METHODS: A systematic literature search was conducted in PubMed and ClinicalTrials.gov up to November 2023, searching for \"ultrasound\" AND \"brain tumor\". The search yielded 1446 results. After screening by title and abstract, followed by full-text screening (n = 48), 35 studies were included in the analysis.
RESULTS: Our analysis includes data from 11 published studies and 24 ongoing trials. The predominant focus of these studies is on glioma, including GMB and astrocytoma. One paper investigated brain metastasis from breast cancer. Evidence indicates that FUS facilitates BBB opening and enhances drug uptake following sonication. Exploration of FUS in the pediatric population is limited, with no published studies and only three ongoing trials dedicated to this demographic.
CONCLUSIONS: FUS is a promising strategy to safely disrupt the BBB, enabling precise and non-invasive lesion targeting, and enhance drug delivery. However, pharmacokinetic studies are required to quantitatively assess improvements in drug uptake. Most studies are phase I clinical trials, and long-term follow-up investigating patient outcomes is essential to evaluate the clinical benefit of this treatment approach. Further studies involving diverse populations and pathologies will be beneficial.

Pediatric brain tumors, particularly those affecting the brainstem, present a significant challenge due to their intricate anatomical location and diverse classification. This review explores the classification, anatomical considerations, and surgical approaches for pediatric brainstem tumors, focusing on recent updates from the World Health Organization (WHO) classification. Brainstem tumors encompass a spectrum from diffuse gliomas to focal intrinsic and exophytic types, each presenting unique clinical and surgical challenges. Surgical strategies have evolved with advancements in neuroimaging and surgical techniques, emphasizing approaches such as neuroendoscopy and tailored incisions to minimize damage to critical structures. Despite the complexities involved, recent developments offer promising outcomes in tumor resection and patient management, highlighting ongoing advancements in neurosurgical care for pediatric brain tumors.

Glioblastoma (GBM) is a primary CNS tumor that is highly lethal in adults and has limited treatment options. Despite advancements in understanding the GBM biology, the standard treatment for GBM has remained unchanged for more than a decade. Only 6.8% of patients survive beyond five years. Telomerase, particularly the hTERT promoter mutations present in up to 80% of GBM cases, represents a promising therapeutic target due to its role in sustaining telomere length and cancer cell proliferation. This review examines the biology of telomerase in GBM and explores potential telomerase-targeted therapies. We conducted a systematic review following the PRISMA-P guidelines in the MEDLINE/PubMed and Scopus databases, from January 1995 to April 2024. We searched for suitable articles by utilizing the terms \"GBM\", \"high-grade gliomas\", \"hTERT\" and \"telomerase\". We incorporated studies addressing telomerase-targeted therapies into GBM studies, excluding non-English articles, reviews, and meta-analyses. We evaluated a total of 777 records and 46 full texts, including 36 studies in the final review. Several compounds aimed at inhibiting hTERT transcription demonstrated promising preclinical outcomes; however, they were unsuccessful in clinical trials owing to intricate regulatory pathways and inadequate pharmacokinetics. Direct hTERT inhibitors encountered numerous obstacles, including a prolonged latency for telomere shortening and the activation of the alternative lengthening of telomeres (ALT). The G-quadruplex DNA stabilizers appeared to be potential indirect inhibitors, but further clinical studies are required. Imetelstat, the only telomerase inhibitor that has undergone clinical trials, has demonstrated efficacy in various cancers, but its efficacy in GBM has been limited. Telomerase-targeted therapies in GBM is challenging due to complex hTERT regulation and inadequate inhibitor pharmacokinetics. Our study demonstrates that, despite promising preclinical results, no Telomerase inhibitors have been approved for GBM, and clinical trials have been largely unsuccessful. Future strategies may include Telomerase-based vaccines and multi-target inhibitors, which may provide more effective treatments when combined with a better understanding of telomere dynamics and tumor biology. These treatments have the potential to be integrated with existing ones and to improve the outcomes for patients with GBM.

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients\' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

OBJECTIVE: Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.
METHODS: A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.
RESULTS: Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.
CONCLUSIONS: While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.

UNASSIGNED: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer\'s disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
UNASSIGNED: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
UNASSIGNED: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
UNASSIGNED: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Medulloblastoma, an embryonal tumor located in the posterior fossa of the brain, originates from the neuro-epidermal layer of the cerebellum. It is the most prevalent malignant tumor in children, while it is rare in adults and predominantly affects males. Multimodal therapeutic interventions, such as surgery, radiotherapy, and chemotherapy, have substantially enhanced the prognosis of this condition. Extraneural metastases are infrequent. We present a case of medulloblastoma relapse with nodal metastasis in a 28-year-old adult.

Central neurocytoma (CN) is a rare, low-grade, neuronal tumor frequently encountered in young adults. Complete surgical resection is the treatment of choice; however, it is associated with grave postoperative complications in a quarter of patients, including neurological (motor weakness, memory deficit, aphasia, and seizure) as well as regional (hydrocephalus, hematoma, infection, and subcutaneous hydrops) complications. Herein, we present a case of a 35-year-old female who presented with decreased vision for the last 7-8 days and headache over the last 1-1.5 years. An ophthalmologic examination suggested papilledema. Magnetic resonance imaging (MRI) of the brain illustrated a well-circumscribed, large, lobulated, altered signal intensity midline intraventricular lesion (72 × 68 mm) attached to the septum pellucidum near the foramen of Monro (FoM) most likely to be CN. The patient underwent complete surgical resection but required re-exploration the next day for hematoma removal due to intraventricular hemorrhage. Over the next 40 days, the patient developed hydrocephalus with transtentorial herniation and succumbed. Histopathological examination (HPE) was suggestive of CN and immunohistochemistry (IHC) was strongly positive for synaptophysin, thus confirming the diagnosis of CN.

BACKGROUND: Low-income countries (LICs) and lower-middle-income countries (LMICs) are presented with unique challenges and opportunities when performing awake craniotomy (AC) for brain tumors. These circumstances arise from factors that are financial, infrastructural, educational, personnel, and sociocultural in nature.
METHODS: We performed a systematic narrative review of series on AC for intra-axial brain tumors in LICs/LMICs using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, focusing on the challenges and opportunities in these settings. The PubMed, Scopus, and Web of Science databases were searched.
RESULTS: After initially identifying 74 studies, inclusion-exclusion criteria were applied, leaving a total of 14 studies included in the review. These involved 409 patients who underwent AC in LICs/LMICs. These series were from India, Ghana, Nigeria, Iran, Pakistan, Morocco, the Philippines, and Egypt. The most common pathology encountered were gliomas (10-70%). Most studies (11/14, 78.5%) reported on their technique of cortical-subcortical mapping. All reported on motor mapping and 8 of these performed language mapping. The most common outcomes reported were seizure and neurologic deficits, and longest follow-up was at 1 year. Challenges noted were lack of equipment and trained personnel, need for validated tests for the local setting, and sociocultural factors. Opportunities identified were volume for training, technique innovation, and international collaboration.
CONCLUSIONS: There are numerous challenges and opportunities that arise when performing AC in LICs/LMICs. A collaborative approach toward harnessing the opportunities, and seeking creative solutions to address the challenges, would provide an ideal mechanism toward advancing neurosurgical care and specialty worldwide.