Central nervous system (CNS) tumors, encompassing a diverse array of neoplasms in the brain and spinal cord, pose significant therapeutic challenges due to their intricate anatomy and the protective presence of the blood-brain barrier (BBB). The primary treatment obstacle is the effective delivery of therapeutics to the tumor site, which is hindered by multiple physiological, biological, and technical barriers, including the BBB. This comprehensive review highlights recent advancements in material science and nanotechnology aimed at surmounting these delivery challenges, with a focus on the development and application of nanomaterials. Nanomaterials emerge as potent tools in designing innovative drug delivery systems that demonstrate the potential to overcome the limitations posed by CNS tumors. The review delves into various strategies, including the use of lipid nanoparticles, polymeric nanoparticles, and inorganic nanoparticles, all of which are engineered to enhance drug stability, BBB penetration, and targeted tumor delivery. Additionally, this review highlights the burgeoning role of theranostic nanoparticles, integrating therapeutic and diagnostic functionalities to optimize treatment efficacy. The exploration extends to biocompatible materials like biodegradable polymers, liposomes, and advanced material-integrated delivery systems such as implantable drug-eluting devices and microfabricated devices. Despite promising preclinical results, the translation of these material-based strategies into clinical practice necessitates further research and optimization.

Gangliogliomas are commonly found pathologies in patients undergoing epilepsy surgery. While resections can be curative, seizure relapses occur. Expression of CD34 and the BRAF V600E mutation are the most common molecular biomarkers found in gangliogliomas, but their influence on seizure outcomes is unclear. We therefore reviewed our experience over two decades to better describe prognostic factors.

We performed a retrospective chart review of all patients operated on for ganglioglioma at our institution since the year 2000. We included patients with preoperative epilepsy and a minimum follow-up of 1 year. Available tumor specimens were immunohistochemically stained for CD34 and BRAF V600E.

We included 62 patients with epilepsy operated for ganglioglioma. Lesionectomies were performed in 32 (51.6%), extended resections in 21 (33.9%), and partial resections in 9 cases (14.5%). Residual tumor mass on postoperative MRI was diagnosed in 21 patients (33.9%). CD34 reactivity was found in 57 patients (91.9%) and the BRAF V600E mutation was detected in 30 patients (48.4%). Patients with a BRAF V600E mutation were younger at the time of epilepsy onset (9.1 years vs. 15.2 years) and surgery (14.5 years vs. 23.7 years). Residual tumor was the largest risk factor for seizure relapses (hazard ratio 8.45) and the BRAF V600E mutation also increased this risk (hazard ratio 3.94).

BRAF V600E status in patients with ganglioglioma-associated epilepsy is a potential biomarker to stratify the risk for seizure relapse after surgery. BRAF V600E-positive patients might benefit from a more aggressive surgical strategy.

Glioblastoma remains one of the most lethal malignancies, largely due to its resistance to standard chemotherapy such as temozolomide. This study investigates a novel resistance mechanism involving glioblastoma stem cells (GSCs) and the polarization of M2-type macrophages, mediated by the extracellular vesicle (EV)-based transfer of Clusterin. Using 6-week-old male CD34+ humanized huHSC-(M-NSG) mice (NM-NSG-017) and glioblastoma cell lines (T98G and U251), we demonstrated that GSC-derived EVs enriched with Clusterin induce M2 macrophage polarization, thereby enhancing temozolomide resistance in glioblastoma cells. Single-cell and transcriptome sequencing revealed close interactions between GSCs and M2 macrophages, highlighting Clusterin as a key mediator. Our findings indicate that Clusterin-rich EVs from GSCs drive glioblastoma cell proliferation and resistance to temozolomide by modulating macrophage phenotypes. Targeting this pathway could potentially reverse resistance mechanisms, offering a promising therapeutic approach for glioblastoma. This study not only sheds light on a critical pathway underpinning glioblastoma resistance but also lays the groundwork for developing therapies targeting the tumor microenvironment. Our results suggest a paradigm shift in understanding glioblastoma resistance, emphasizing the therapeutic potential of disrupting EV-mediated communication in the tumor microenvironment.

Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, and standardized management are of significant clinical importance for extending the survival rates of brain tumor patients. Artificial intelligence (AI), a discipline within computer science, is leveraging its robust capacity for information identification and combination to revolutionize traditional paradigms of oncology care, offering substantial potential for precision medicine. This article provides an overview of the current applications of AI in brain tumors, encompassing the primary AI technologies, their working mechanisms and working workflow, the contributions of AI to brain tumor diagnosis and treatment, as well as the role of AI in brain tumor scientific research, particularly in drug innovation and revealing tumor microenvironment. Finally, the paper addresses the existing challenges, potential solutions, and the future application prospects. This review aims to enhance our understanding of the application of AI in brain tumors and provide valuable insights for forthcoming clinical applications and scientific inquiries.

The gene F3, encoding Tissue Factor (TF), is expressed in many cancers and contributes to their malignancy. Among adult-type diffuse gliomas, IDH1/2 wild-type (IDHwt) glioblastomas (GBM) express more TF than IDH1/2 mutant (IDHmut) gliomas. Tisotumab vedotin (TisVed), an anti-TF antibody conjugated to monomethyl auristatin E, is a therapeutic designed to target cells expressing TF. We therefore sought to determine the therapeutic potential of TisVed in IDHwt vs. IDHmut gliomas.

We treated IDHwt and IDHmut patient-derived glioma cells with control IgG, unconjugated tisotumab (Tis), or TisVed in vitro, followed by cell viability assays and the assessment of TF signaling. We tested Tis and TisVed in mice intracranially engrafted with patient-derived IDHwt and IDHmut gliomas and mice flank engrafted with IDHwt GBM.

TisVed was more active against cultured IDHwt GBM cells than IDHmut glioma cells. This activity was increased by the daily washout of soluble TF secreted by IDHwt GBM cells. Unconjugated Tis had less effect than TisVed, and TF signaling was minimally inhibited. TisVed extended the survival of mice intracranially engrafted with IDHwt GBM (p = 0.006), but not mice with IDHmut glioma (p = 0.88). TisVed also reduced the growth of IDHwt GBM flank xenografts. Tis alone had no antitumor effect in either setting. Notably, both TisVed and Tis were associated with hemorrhage in flank tumors.

TisVed targets high-TF-expressing IDHwt GBM, but not low-TF-expressing IDHmut glioma. This is predominately through the vedotin conjugate rather than inhibition of TF signaling. Though the effect size is modest, TisVed shows anticancer effects against IDHwt GBM. However, there could be complications related to hemostasis and hemorrhage.

Blocking the C-X-C motif chemokine ligand-12/C-X-C motif chemokine receptor-4 (CXCL12/CXCR4) signal offers the potential to induce immunogenic cell death (ICD) and enhance immunotherapy of glioblastoma (GBM). However, traditional intracellular targeted delivery strategies and adenosine-mediated tumor immunosuppression limit its therapeutic efficacy. Herein, we present an acidity-triggered self-assembly nanoplatform based on bioorthogonal reaction to potentiate GBM immunotherapy through dual regulation of metabolism and immune pathways. AMD3100 (CXCR4 antagonist) and CPI-444 (adenosine 2A receptor inhibitor) were formulated into micelles, denoted as AMD@iNPDBCO and CPI@iNPN3, respectively. Upon administration, the pH-sensitive poly(2-azepane ethyl methacrylate) group of AMD@iNPDBCO responds to the acidic tumor microenvironment, exposing the DBCO moiety, resulting in highly efficient bioorthogonal reaction with azide group on CPI@iNPN3 to form large-sized aggregates, ensuring extracellular drug release. The combination of AMD3100 and CPI-444 contributes to ICD induction, dendritic cell maturation, and immunosuppressive milieu alleviation by reducing tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, leading to a robust antitumor response, thereby significantly prolonging survival in orthotopic GBM-bearing mice. Furthermore, the nanoplatform remarkably amplifies immuno-radiotherapy by potently evoking cytotoxic CD8+ T cell priming, and synergized with immune checkpoint blockade by delaying CD8+ T cell exhaustion. Our work highlights the potential of the in situ assembly nanoplatform tailored for delivery of extracellular-targeted therapeutic agents for boosting GBM immunotherapy.

This study aimed to investigate the knowledge, attitude, and practice (KAP) regarding spinal cord tumors among both patients and their family members.

A cross-sectional survey was conducted at the Department of Neurosurgery, Beijing Jishuitan Hospital, between August 1, 2023, and January 31, 2024 using a self-designed questionnaire.

A total of 489 valid questionnaires were analyzed, including 219 (44.79%) from patients. The mean knowledge, attitude, and practice scores were 11.09 ± 6.64 (possible range: 0-28), 18.61 ± 1.92 (possible range: 6-30), and 33.58 ± 4.34 (possible range: 8-40), respectively. Multivariate logistic regression analysis revealed that urban residency (OR = 1.904, 95% CI: 1.113-3.314, P = 0.020) and higher monthly per capita income (OR = 3.779, 95% CI: 1.697-8.599, P = 0.001) were independent predictors of proactive practice. Path analysis demonstrated that knowledge (β = 0.11, P < 0.001), monthly per capita income (β = 1.15, P < 0.001), and marital status (β = -0.93, P = 0.039) directly influenced practice behaviors.

Patients and their families demonstrated suboptimal knowledge, negative attitude and proactive practice towards spinal cord tumors. Efforts should be made to enhance education and awareness programs targeting both patients and their families for improving knowledge and fostering positive attitudes.

Nowadays, the genetic and biomolecular profile of neoplasms-related with their biological behaviour-have become a key issue in oncology, as they influence many aspects of both diagnosis and treatment. In the neuro-oncology field, neuroradiological research has recently explored the potential of non-invasively predicting the molecular phenotype of primary brain neoplasms, particularly gliomas, based on magnetic resonance imaging (MRI), using both conventional and advanced imaging techniques. Among these, diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS) and susceptibility-weighted imaging (SWI) and have been used to explore various aspects of glioma biology, including predicting treatment response and understanding treatment-related changes during follow-up imaging. Recently, intratumoral susceptibility signals (ITSSs)-visible on SWI-have been recognised as an important new imaging tool in the evaluation of brain gliomas, as they offer a fast and simple non-invasive window into their microenvironment. These intratumoral hypointensities reflect critical pathological features such as microhemorrhages, calcifications, necrosis and vascularization. Therefore, ITSSs can provide neuroradiologists with more biological information for glioma differential diagnosis, grading and subtype differentiation, providing significant clinical support in prognosis assessment, therapeutic management and treatment response evaluation. This review summarizes recent advances in ITSS applications in glioma assessment, emphasizing both its potential and limitations while referencing key studies in the field.

In this investigation, to determine their potential as specific antibacterial agents, Schiff's bases (LT-SB1-23 and SB1-SB12) and novel quinoline-isatin hybrids were subjected to microbiological testing. The in-vitro screening against bacterial strains (Escherichia coli, Enterococcus faecalis, Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella typhi) exhibited their antibacterial potential with many of the compounds showing inhibition range of 90-100 % at 200 μg/mL, against most of the tested strains. The MIC values of some of the compounds showed good antibacterial efficacy with values ranging from 32 to 128 μg/mL. Their bacterial growth inhibitory potential was further supported by disk diffusion and growth curve assays. Interestingly, one of the Schiff's bases (LT-SB7) displayed strong synergistic activity against E. coli and S. typhi with 16-64 folds reduction in MIC values. Additionally, it exhibited up to 85 % suppression of biofilm at ½MIC against AA209 environmental bacterial isolate and reduced the development of multidrug-resistant bacterial isolates. Promising compound LT-SB7 underwent 100 ns molecular dynamics simulations with biofilm-causing protein (PDB ID: 7C7U) to assess conformational changes and complex stability. Overall, this study identified compounds as effective antibacterial alternatives for the future.

Meningiomas, the most common primary intracranial neoplasms, account for more than one-third of primary CNS tumors. While traditionally viewed as benign, meningiomas can be associated with considerable morbidity, and specific meningioma subgroups display more aggressive behavior with higher recurrence rates. The risk stratification for recurrence has been primarily associated with the World Health Organization (WHO) histopathologic grade and extent of resection. However, a growing body of literature has highlighted the value of molecular characteristics in assessing recurrence risk. While maintaining the previous classification system, the 5th edition of the 2021 WHO Classification of Central Nervous System tumors (CNS5) book expands upon the molecular information in meningiomas to help guide management. The WHO CNS5 stratifies meningioma into 3 grades (1-3) based on histopathology criteria and molecular profile. The telomerase reverse transcriptase promoter mutations and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletions now signify a grade 3 meningioma with increased recurrence risk. Tumor location also correlates with underlying mutations. Cerebral convexity and most spinal meningiomas carry a 22q deletion and/or NF2 mutations, while skull base meningiomas have AKT1, TRAF7, SMO, and/or PIK3CA mutations. MRI is the primary imaging technique for diagnosing and treatment-planning of meningiomas, while DOTATATE PET imaging offers supplementary information beyond anatomic imaging. Herein, we review the evolving molecular landscape of meningiomas, emphasizing imaging/genetic biomarkers and treatment strategies relevant to neuroradiologists.

Lower-grade (WHO grades 2-3) gliomas typically affect young and middle-aged adults and exhibit isocitrate dehydrogenase (IDH) mutations. For such patients, symptoms related to the tumor and associated treatment contribute to morbidity and erode quality of life. With improved treatment, a better understanding of these effects over time is critically needed. Existing data characterizing unmet needs of lower-grade glioma patients is limited and little consensus exists on addressing these needs in clinical practice.

In order to better identify and address the unmet needs of lower-grade glioma patients, focus groups among patients and caregivers were initially conducted among patients treated at a single academic center. A semi-structured interview guide to comprehensively understand unmet needs was then developed. Each patient-defined unmet need was categorized into domains through qualitative content analysis. In parallel, a database of established local and regional community-based resources was established, and a dedicated resource specialist provided patient-specific referrals and follow-up.

Eighty-five patients were interviewed. Median age was 41 years and the median time from tumor diagnosis was 63 months. Approximately 68% had a WHO grade 2 tumor and 60% were off therapy. Qualitative analysis of interview content identified 5 overarching domains of unmet need: Psychosocial; Neurologic/Cognitive; Lifestyle; Financial; and Other Medical. At least one unmet need was identified by 71% of participants and the most common domains were Psychosocial (40.7%) and Lifestyle (34.9%).

Our program begins to address frequently unmet survivorship needs of lower-grade glioma patients that spanned 5 major domains. Further research aimed to better define and address unmet needs among these patients is warranted.

Conventional treatments, including surgery, radiotherapy and chemotherapy, have many limitations in the prognosis of glioma patients. Atorvastatin (ATOR) has a significant inhibitory effect on glioma malignancy. Thus, ATOR may play a key role in the search for new drugs for the effective treatment of gliomas.

U87 cells were treated with different doses of ATOR and transfected. Viability was assessed using MTT, proliferative ability was determined using the colony formation test, Bax and Bcl-2 were identified using Western blot, apoptosis was identified using flow cytometry, and U87 cell migration and invasion were detected using the Transwell assay. Glucose uptake, lactate secretion, and ATP production in U87 cell culture medium were quantified. The positive rates of IFN-γ and TNF-α in CD8T were measured through flow cytometry. Subcutaneous injection of U87 cells was carried out to construct an in vivo mouse model of gliom, followed by HE staining to assess the effects of ATOR and miR-125a-5p on tumor development.

ATOR blocked the viability, proliferation, migration, and invasion of U87 cells through the miR-125a-5p/TXLNA axis, and suppressed glycolysis and immune escape of glioma cells. Furthermore, overexpressing miR-125a-5p enhanced the anti-tumor effect of ATOR in vivo.

ATOR blocks glioma progression by modulating the miR-125a-5p/TXLNA axis, further demonstrating that ATOR provides an effective therapeutic target for the treatment of glioma.

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a universally lethal prognosis despite maximal standard therapies. Here, we present a consensus treatment protocol based on the metabolic requirements of GBM cells for the two major fermentable fuels: glucose and glutamine. Glucose is a source of carbon and ATP synthesis for tumor growth through glycolysis, while glutamine provides nitrogen, carbon, and ATP synthesis through glutaminolysis. As no tumor can grow without anabolic substrates or energy, the simultaneous targeting of glycolysis and glutaminolysis is expected to reduce the proliferation of most if not all GBM cells. Ketogenic metabolic therapy (KMT) leverages diet-drug combinations that inhibit glycolysis, glutaminolysis, and growth signaling while shifting energy metabolism to therapeutic ketosis. The glucose-ketone index (GKI) is a standardized biomarker for assessing biological compliance, ideally via real-time monitoring. KMT aims to increase substrate competition and normalize the tumor microenvironment through GKI-adjusted ketogenic diets, calorie restriction, and fasting, while also targeting glycolytic and glutaminolytic flux using specific metabolic inhibitors. Non-fermentable fuels, such as ketone bodies, fatty acids, or lactate, are comparatively less efficient in supporting the long-term bioenergetic and biosynthetic demands of cancer cell proliferation. The proposed strategy may be implemented as a synergistic metabolic priming baseline in GBM as well as other tumors driven by glycolysis and glutaminolysis, regardless of their residual mitochondrial function. Suggested best practices are provided to guide future KMT research in metabolic oncology, offering a shared, evidence-driven framework for observational and interventional studies.

Gliomas are a heterogeneous group of brain tumors, among which the most aggressive subtype is glioblastoma, accounting for 60% of cases in adults. Available systemic treatment options are few and ineffective, so new approaches to therapies for glioblastoma are in high demand. In total, 131 patients with diffuse glioma were studied. Paired tumor–normal samples were sequenced on the Illumina platform; the panel included 812 genes associated with cancer development. Molecular profiles in clinically distinct groups were investigated. In low-grade glioma (LGG) patients (n = 18), the most common mutations were IDH1/2 (78%), ATRX (33%), TP53 (44%), PIK3CA (17%), and co-deletion 1p/19q (22%). In high-grade glioma (HGG) patients (n = 113), more frequently affected genes were CDKN2A/B (33%), TERTp (71%), PTEN (60%), TP53 (27%), and EGFR (40%). The independent predictors of better prognosis were tumor grade and IDH1/2 mutations. In IDH—wildtype glioblastoma patients, a history of other precedent cancer was associated with worse overall survival (OS), while re-operation and bevacizumab therapy increased OS. Also, among genetic alterations, TERTp mutation and PTEN deletion were markers of poor prognosis. Nine patients received molecular targeted therapy, and the results were evaluated. The search for molecular changes associated with tumor growth and progression is important for diagnosis and choice of therapy.

Previous research has demonstrated correlations between the complex types and functions of brain cells and the etiology of glioma. However, the causal relationship between gene expression regulation in specific brain cell types and glioma risk, along with its therapeutic implications, remains underexplored.

Utilizing brain cell type-specific cis-expression quantitative trait loci (cis-eQTLs) and glioma genome-wide association study (GWAS) datasets in conjunction with Mendelian randomization (MR) and colocalization analyses, we conducted a systematic investigation to determine whether an association exists between the gene expression of specific brain cell types and the susceptibility to glioma, including its subtypes. Additionally, the potential pathogenicity was explored utilizing mediation and bioinformatics analyses. This exploration ultimately led to the identification of a series of brain cell-specific therapeutic targets.

A total of 110 statistically significant and robust associations were identified through MR analysis, with most genes exhibiting causal effects exclusively in specific brain cell types or glioma subtypes. Bayesian colocalization analysis validated 36 associations involving 26 genes as potential brain cell-specific therapeutic targets. Mediation analysis revealed genes indirectly influencing glioma risk via telomere length. Bioinformatics analysis highlighted the involvement of these genes in glioma pathogenesis pathways and supported their enrichment in specific brain cell types.

This study, employing an integrated approach, demonstrated the genetic susceptibility between brain cell-specific gene expression and the risk of glioma and its subtypes. Its findings offer novel insights into glioma etiology and underscore potential therapeutic targets specific to brain cell types.

Glioma is a highly lethal brain cancer in humans. Despite advancements in treatment, the prognosis for patients remains unfavorable. Epigenetic factors, along with their interactions and non-coding RNAs (ncRNAs), are crucial in glioma cells' development and aggressive characteristics. MicroRNAs (miRNAs) are a class of small non-coding RNAs (ncRNAs) that modulate the expression of various genes by binding to target mRNA molecules. They play a critical role in regulating essential biological mechanisms such as cell proliferation and differentiation, cell cycle, and apoptosis. MiR-146a/miR-146b is a significant and prevalent miRNA whose expression alterations are linked to various pathological changes in cancer cells, as well as the modulation of several cellular signaling pathways, including NF-κB, TGF-β, PI3K/Akt, and Notch-1. Scientists may identify novel targets in clinical settings by studying the complicated link between Mir-146a/mir-146b, drug resistance, molecular pathways, and pharmacological intervention in gliomas. Additionally, its interactions with other ncRNAs, such as circular RNA and long non-coding RNA, contribute to the pathogenesis of glioma. As well as miR-146 holds potential as both a diagnostic and therapeutic biomarker for patients with this condition. In the current review, we investigate the significance of miRNAs in the context of glioma, with a particular focus on the critical role of Mir-146a/mir-146b in glioma tumors. Additionally, we examined the clinical relevance of this miRNA, highlighting its potential implications for diagnosis and treatment.

Glioblastoma (GBM) is the most common intracranial malignancy. SUV39H1 encodes a histone H3 lysine 9 methyltransferase that acts as an oncogene in several cancers; however, its role in GBM remains unknown. We obtained GBM transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database on the UCSC Xena platform to perform differential and enrichment analyses of genes in the SUV39H1 high- and low-expression groups to construct a prognostic risk model. Analysis of SUV39H1 related biological processes in GBM was performed by gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). High- and low-risk subgroup mutation signatures were analyzed using maftools. Immune infiltration was evaluated using IOBR and CIBERSORT algorithms. We analyzed the cell types and intercellular communication networks in glioma stem cells (GSCs) using scRNA-seq. The effects on GBM cells and GSCs after inhibition of SUV39H1 were investigated in vitro. SUV39H1 was significantly overexpressed in GBM and associated with poor prognosis. SUV39H1-related differentially expressed genes were enriched in immune and inflammation related pathways, and GSEA revealed that these genes were significantly enriched in signaling pathways such as IL-18, oxidative phosphorylation, and regulation of TP53 activity. Mutational analysis revealed frequent alterations in TP53 and PTEN expression. In addition, the infiltration abundances of the five immune cell types were significantly different between the high- and low-expression groups. Analysis of cellular communication networks by scRNA-seq revealed a strong interaction between CRYAB-GSC and PTPRZ1-GSC in GSCs. In vitro experiments verified that knockdown of SUV39H1 inhibited the viability and proliferation of U87 and U251 glioblastoma cells and downregulated the expression of stemness markers Nestin and SOX2 in CSC1589 and TS576 GSC lines. Increased SUV39H1 expression is associated with immune cell infiltration and poor prognosis in patients with GBM. Inhibition of SUV39H1 restrains GBM growth and reduces the stem cell properties of GSC. Thus, SUV39H1 might be a prognostic predictor and immunotherapeutic target in patients with GBM.

Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.

This study proposes a hybrid model that integrates MobileNetV2 for feature extraction with a Support Vector Machine (SVM) classifier for the classification of brain tumors. The model was trained and validated using the Kaggle MRI brain tumor dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, and no tumor. MobileNetV2's efficient architecture was leveraged for feature extraction, and SVM was used to enhance classification accuracy.

The proposed hybrid model showed excellent results, achieving Area Under the Curve (AUC) scores of 0.99 for glioma, 0.97 for meningioma, and 1.0 for both pituitary tumors and the no tumor class. These findings highlight that the MobileNetV2-SVM hybrid not only improves classification accuracy but also reduces computational overhead, making it suitable for broader clinical use.

The MobileNetV2-SVM hybrid model demonstrates substantial potential for enhancing brain tumor diagnostics by offering a balance of precision and computational efficiency. Its ability to maintain high accuracy while operating efficiently could lead to better outcomes in medical practice, particularly in resource limited settings.

Research on central nervous system tumors (CNSTs) has a significant impact on the diagnosis and prognosis of patients. Currently, CNSTs are classified according to the schema proposed by the World Health Organization (WHO), which considers clinical, histopathological, and molecular characteristics, highlighting the importance of tumor biology for accurate diagnosis and optimal treatment approaches. Despite these advances, assessing DNA ploidy-a marker of tumor aggressiveness-remains complex in CNSTs. This review investigates the utility of DNA index (DNAi) and DNA ploidy analysis by flow cytometry in diagnosing CNSTs and prognosing their outcomes. We systematically reviewed studies in the PubMed database from 1990 to the present using the keywords "DNA Index", "Brain", "Flow cytometry", and "Ploidy". We identified 151 studies, 36 of which met our inclusion criteria. We found considerable variation in sample sizes and methodological variation across the studies. Discrepancies between the reported DNAi and ploidy values were observed. Aneuploidy is generally associated with more aggressive tumors, although exceptions exist. Higher DNAi levels correlate with increased malignancy, notably in glioblastomas, astrocytomas, and meningiomas, whereas diploid astrocytomas and oligodendrogliomas are associated with shorter survival rates. DNA ploidy assessment via flow cytometry could predict CNST behavior, yet methodological issues with tissue selection, adequate control samples, and technique variability remain. DNAi and ploidy assessments show promise as prognostic markers in CNSTs. However, the standardization of flow cytometry protocols and alignment with the current WHO classification schema are essential steps to integrate ploidy analysis in routine CNST assessment.

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor with a poor prognosis and limited survival. Patients with GBM have a high demand for palliative care. In our present case, a 21-year-old female GBM patient received inpatient palliative care services including symptom management, mental and psychological support for the patient, psychosocial and clinical decision support for her family members, and pre- and post-death bereavement management for the family. Furthermore, we provided the family members with comprehensive psychological preparation for the patient's demise and assisted the patient's family throughout the mourning period.The aim of this study is to provide a reference and insights for the clinical implementation of palliative care for patients with malignant brain tumors.

To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase (IDH) classification in adult diffuse gliomas.

A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann-Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan-Meier test.

The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade (P < .01). Subsequent application of Dunn's test showed significant differences in ADC mean among each histological grade (P < .01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas (P < .01). The Mann-Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification (P < .01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI's ADC mean parameter shows significant distinctions in both histological grade and IDH classification.

TRAF interacting protein with forkhead associated domain (TIFA) influence progression of many cancers. However, its role in glioma remains to be explored. This study investigated the function of TIFA in glioma.

The TIFA expression in glioma and patient outcomes were analyzed using online database. Gene set enrichment analysis (GSEA) revealed related mechanisms of TIFA in glioma. TIFA's effects on glioma glycolysis and growth were assessed using in vitro and in vivo experiments. Moreover, luciferase reporter and ChIP were employed to explore the interactions among E2F1, GLUT1, HK2, and LDHA. The subcutaneous xenograft assay further elaborated the effects of TIFA in glioma.

We found overexpressed TIFA in glioma. Moreover, the high TIFA expression was associated with poor prognosis of glioma. Furthermore, GSEA indicated that overexpressed TIFA promoted E2F1 and glycolysis. Knockdown of TIFA decreased glioma development in cell and mice. TIFA knockdown down-regulated the expression of E2F1, GLUT1, HK2, and LDHA.

The study provides evidence that TIFA regulates E2F1 expression in glioma cells and promotes the proliferation, migration, and glycolysis. TIFA might be an advantageous therapeutic strategy against glioma.

In recent decades, new first and subsequent lines of anticancer treatment and supportive care have improved survival for children with cancer. We investigated recent temporal changes in the incidence of relapse and survival after relapse among children with cancer in Denmark.

This register-based study included 2890 children diagnosed before age 15 years with haematological cancers and solid tumours (2001-2021) and central nervous system (CNS) tumours (2010-2021). We used the Aalen-Johansen and Kaplan-Meier estimators to assess cumulative incidence of relapse-defined as cancer recurrence or progression-and survival probability after relapse.

Comparing the periods 2001-2010 and 2011-2021, the 5-year cumulative incidence of relapse decreased from 14% to 11% among children with haematological cancers (p = .07), and from 21% to 18% among children with solid tumours (p = .26). Concurrently, the 5-year survival after relapse increased among children with haematological cancers (from 44% to 61%, p = .03) and solid tumours (from 38% to 46%, p = .25). Among children with malignant CNS tumours, the 5-year cumulative incidence of relapse and the 5-year survival after relapse remained stable (49% and 51%, p = .82; and 20% and 18%, p = .90) comparing 2010-2015 and 2016-2021.

In recent decades in Denmark, improvements were observed in reducing relapse incidence and increasing survival after relapse in children with haematological cancers and solid tumours. However, the persistent survival gap between children who relapse and those who do not across all childhood cancers underlines the need for intensified and highly targeted treatments for children at high risk of relapse.

Glioblastomas are the most common and aggressive form of malignant primary brain tumors in adults. The standard treatment is surgical resection followed by radiotherapy and chemotherapy. Despite optimal treatment methods, the prognosis for patients remains poor. Preoperative determination of glioblastoma margins remains beneficial for the complete removal of the tumor mass. Radiotherapy is essential for post-surgery treatment, but radioresistance is a significant challenge contributing to high mortality rates. Advanced imaging technologies are used to analyze the changes in the peritumoral brain zone (PTZ). Consequently, they may lead to the development of novel therapeutic options, especially targeting the marginal parts of a tumor, which could improve the prognosis of glioblastoma patients. The clinical presentation of glioblastoma is heterogeneous and mostly depends on the location and size of a tumor. Glioblastomas are characterized by both intratumoral cellular heterogeneity and an extensive, diffuse infiltration into the normal tissue bordering a tumor called the PTZ. Neuroimaging techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), proton magnetic resonance spectroscopy (1H MRS), and chemical exchange saturation transfer (CEST) are useful methods in the evaluation of the tumor infiltration and thus the resection margin.

Brain tumors have been deadly cancers for years, and in most cases they are difficult to diagnose in their early stages. For this reason, researchers need to develop low-cost, sensitive methods for examining cancer biomarkers. Such biomarkers include microRNA. MicroRNA expression in various body fluids shows a high correlation with cancer. A number of studies have demonstrated changes in microRNA expression in cerebrospinal fluid and blood samples from patients with brain tumors. New biomarkers such as microRNAs may help diagnose brain tumors at the very beginning of the disease, enabling early treatment and increasing the chances of survival. This review describes the diagnostic role of microRNAs and the prospects for their use as biomarkers in patients with brain tumors.

Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential.

To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI).

Retrospective study.

70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)).

3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values.

Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed.

Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction.

Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%).

Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI.

4 TECHNICAL EFFICACY: Stage 2.

A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologists. In this retrospective study, a novel auto-segmentation method is proposed to address these problems. To assess the method's applicability across diverse scenarios, we conducted its development and evaluation using a cohort of 148 eligible patients drawn from four multicenter datasets and retrospective data collection including noncontrast CT, multisequence MRI scans, and corresponding medical records. All patients were diagnosed with histologically confirmed high-grade glioma (HGG). A deep learning-based method (PKMI-Net) for automatically segmenting gross tumor volume (GTV) and clinical target volumes (CTV1 and CTV2) of GBMs was proposed by leveraging prior knowledge from multimodal imaging. The proposed PKMI-Net demonstrated high accuracy in segmenting, respectively, GTV, CTV1, and CTV2 in an 11-patient test set, achieving Dice similarity coefficients (DSC) of 0.94, 0.95, and 0.92; 95% Hausdorff distances (HD95) of 2.07, 1.18, and 3.95 mm; average surface distances (ASD) of 0.69, 0.39, and 1.17 mm; and relative volume differences (RVD) of 5.50%, 9.68%, and 3.97%. Moreover, the vast majority of GTV, CTV1, and CTV2 produced by PKMI-Net are clinically acceptable and require no revision for clinical practice. In our multicenter evaluation, the PKMI-Net exhibited consistent and robust generalizability across the various datasets, demonstrating its effectiveness in automatically segmenting GBMs. The proposed method using prior knowledge in multimodal imaging can improve the contouring accuracy of GBMs, which holds the potential to improve the quality and efficiency of GBMs' radiotherapy.

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.

Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.

HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876).

HM-MRI can aid in non-invasive diagnosis of gliomas.

Susceptibility-weighted imaging (SWI) has become a standard component of most brain MRI protocols. While traditionally used for detecting and characterising brain hemorrhages typically associated with stroke or trauma, SWI has also shown promising results in glioma assessment. Numerous studies have highlighted SWI's role in differentiating gliomas from other brain lesions, such as primary central nervous system lymphomas or metastases. Additionally, SWI aids radiologists in non-invasively grading gliomas and predicting their phenotypic profiles. Various researchers have suggested incorporating SWI as an adjunct sequence for predicting treatment response and for post-treatment monitoring. A significant focus of these studies is on the detection of intratumoural susceptibility signals (ITSSs) in gliomas, which are indicative of microhemorrhages and vessels within the tumour. The quantity, distribution, and characteristics of these ITSSs can provide radiologists with more precise information for evaluating and characterising gliomas. Furthermore, the potential benefits and added value of performing SWI after the administration of gadolinium-based contrast agents (GBCAs) have been explored. This review offers a comprehensive, educational, and practical overview of the potential applications and future directions of SWI in the context of glioma assessment. CLINICAL RELEVANCE STATEMENT: SWI has proven effective in evaluating gliomas, especially through assessing intratumoural susceptibility signal changes, and is becoming a promising, easily integrated tool in MRI protocols for both pre- and post-treatment assessments. KEY POINTS: • Susceptibility-weighted imaging is the most sensitive sequence for detecting blood and calcium inside brain lesions. • This sequence, acquired with and without gadolinium, helps with glioma diagnosis, characterisation, and grading through the detection of intratumoural susceptibility signals. • There are ongoing challenges that must be faced to clarify the role of susceptibility-weighted imaging for glioma assessment.

Posterior fossa pediatric low-grade glioma involving the brainstem and cerebellar peduncles (BS-pLGG) are a subgroup with higher risks at surgery. We retrospectively analyzed the role of surgery in the interdisciplinary armamentarium of treatment options in our institutional series of BS-pLGG with various degrees of brainstem involvement.

We analyzed data of 52 children with BS-pLGG after surgical intervention for clinical/molecular characteristics, neurological outcome, factors influencing recurrence/progression pattern, and tumor volumetric analysis of exclusively surgically treated patients to calculate tumor growth velocity (TGV). Tumors were stratified according to primary tumor origin in four groups: (1) cerebellar peduncle, (2) 4th ventricle, (3) pons, (4) medulla oblongata.

The mean FU was 6.44 years. Overall survival was 98%. The mean PFS was 34.07 months. Two patients had biopsies only. Fifty-two percent of patients underwent remission or remained in stable disease (SD) after initial surgery. Patients with progression underwent further 23 resections, 15 chemotherapies, 4 targeted treatments, and 2 proton radiations. TGV decreased after the 2nd surgery compared to TGV after the 1st surgery (p < 0.05). The resection rates were significantly higher in Groups 1 and 2 and lowest in medulla oblongata tumors (Group 4) (p < 0.05). More extended resections were achieved in tumors with KIAA1549::BRAF fusion (p = 0.021), which mostly occurred in favorable locations (Groups 1 and 2). Thirty-one patients showed postoperatively new neurological deficits. A total of 27/31 improved within 12 months. At the end of FU, 6% had moderate deficits, 52% had mild deficits not affecting activities, and 36% had none. Fifty percent of patients were free of disease or showed remission, 38% were in SD, and 10% showed progression.

The first surgical intervention in BS-pLGG can control disease alone in overall 50% of cases, with rates differing greatly according to location (Groups 1 > 2 > 3 > 4), with acceptable low morbidity. The second look surgery is warranted except in medullary tumors. With multimodality treatments almost 90% of patients can obtain remission or stable disease after > 5 years of follow-up. An integrated multimodal and multidisciplinary approach aiming at minimal safe residual disease, combining surgery, chemo-, targeted therapy, and, as an exception, radiation therapy, is mandatory.

Metastasis of World Health Organization (WHO) grade II or grade II meningiomas are rare. The aim of this study was to investigate their incidence, associated risk factors, and treatment course.

Patients with surgically resected WHO grade II or grade III meningiomas were reviewed based on histopathology with the 2016 WHO criteria. Metastasis was diagnosed through whole body image scan followed by surgical resection or biopsy. Clinical factors were analyzed for their association with metastasis.

Among the 131 enrolled patients, metastasis was diagnosed after tumor relapse in 7 (incidence rate 3.6%) at a mean 30.9 months after the initial surgery. The metastasis after tumor relapse group had the worst overall survival, followed by tumor relapse without metastasis and nonrelapse groups (P < 0.001). The independent factors associated with metastasis were major vessel compromise by primary tumors (hazard ratio [HR] = 9.9, P = 0.035), tumor relapse time less than 24 months (HR = 7.0, P = 0.036), and subtotal resection without adjuvant radiotherapy to the primary tumor (HR = 3.5, P = 0.047). Neither grading nor histochemical staining was significantly associated with metastasis, whereas higher vascularity seemed to be more common in metastatic lesions than primary tumors.

The presence of metastasis contributed to poor outcomes and was related to earlier tumor relapse and major vessel compromise. Subtotal resection should be followed by adjuvant radiotherapy to reduce the risk of metastasis. Further research is warranted to identify circulating or pathologic biomarkers for the early detection of metastasis.

Meningiomas are the most common primary intracranial tumors in adults and typically have a slow-growing and benign nature. However, there is also a substantial subset of meningiomas that shows aggressive clinical behavior and is refractory to standard treatment modalities, which are still limited to surgery and/or radiotherapy. Despite intensive research, no systemic treatment options are yet available in the clinic for these challenging tumors, resulting in poor patient outcome. Intensive research on the molecular pathogenesis of meningiomas has led to improved diagnostic tools, but so far there is no standardized implementation for the molecular profiling of these tumors for clinical practice. Recent research advances have also focused on the immunophenotyping of meningiomas, leading to several clinical trials examining the use of immune checkpoint blockade therapy in patients with clinically aggressive subtypes. In this review, we aim to summarize the current knowledge on the molecular and immunological landscape of meningiomas in detail and provide current and progressive ideas for future directions.

Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored.

To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria.

Retrospective.

A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria.

Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery.

Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis).

We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05.

In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis.

ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation.

3 TECHNICAL EFFICACY: Stage 2.

Glioblastoma (GBM), an extremely aggressive and prevalent malignant brain tumor, remains a challenge to treat. Despite a multimodality treatment approach, GBM recurrence remains inevitable, particularly with the emergence of temozolomide (TMZ) resistance and limited treatment options. Surprisingly, previous studies show that a history of allergies, atopy, or asthma is inversely associated with GBM risk. Further, the electronic medical record at the University Hospital of Lausanne showed that the GBM patients taking antihistamine during treatment had better survival. Histamine is an essential neurotransmitter in the brain and plays a significant role in regulating sleep, hormonal balance, and cognitive functions. Elevated levels of histamine and increased histamine receptor expression have been found in different tumors and their microenvironments, including GBM. High histamine 1 receptor (HRH1) expression is inversely related to overall and progression-free survival in GBM patients, further emphasizing the role of histamine in disease progression. This review aims to provide insights into the challenges of GBM treatment, the role of histamine in GBM progression, and the rationale for considering antihistamines as targeted therapy. The review concludes by encouraging further investigation into antihistamine mechanisms and their impact on the tumor microenvironment.