Search Results (379)

Background and Clinical Significance: Gliomas diagnosed during pregnancy are rare, and there are no established guidelines for their management. Effective treatment requires a multidisciplinary approach to balance maternal health and pregnancy preservation. Case Presentation: We here present a case of rapidly progressing glioma in a 33-year-old pregnant woman. The patient initially presented with a generalized tonic–clonic seizure at 21 weeks’ gestation. Imaging revealed a tumor in the right cerebral lobe, involving both cortical and subcortical structures, while magnetic resonance spectroscopy suggested a low-grade glioma. The patient remained clinically stable for two months but then developed severe headaches; MRI showed a worsening mass effect. At 34 weeks’ gestation, an emergency and premature caesarean section was performed under general anesthesia. The patient then underwent a craniotomy for maximal tumor resection, which was histologically and molecularly diagnosed as IDH wild-type glioblastoma (GB). Using qPCR, we found that the GB tissue showed upregulated expression of genes involved in cell structure (GFAP, VIM) and immune response (SSP1, TSPO), as well as increased expression of genes related to potential hormone response (AR, CYP19A1, ESR1, GPER1). After surgery, the patient showed resistance to Stupp protocol therapy, which was substituted with lomustine and bevacizumab combination therapy. Conclusions: This case illustrates that glioma may progress rapidly during pregnancy, but a favorable obstetric outcome is achievable. Management of similar cases should respect both the need for timely treatment and the patient’s informed decision. Full article

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making. Full article

Fluorescence-guided surgery (FGS) was pioneered for glioma and is now established as the standard of care. Gliomas are infiltrative tumours with diffuse margins. FGS provides improved intra-operative identification of tumour margins based on tumour-specific emission visible to the operating surgeon, resulting in increased rates of gross total resection. Multiple fluorescence agents may be used including 5-ALA, fluorescein sodium, and indocyanine green (ICG). This review details the indication, required equipment, mechanism of action, evidence base, limitations, and regulatory issues for each fluorophore as utilised in current clinical practice. FGS for glioma is limited by a reliance on subjective interpretation of visible fluorescence, which is often not present in low-grade glioma (LGG) or at the infiltrative tumour margin. Consequently, there has been a drive to develop enhanced, objective FGS techniques utilising both quantitative fluorescence (QF) imaging systems and novel fluorophores. This review provides an overview of emerging QF imaging systems for FGS. The pipeline for novel fluorophore development is also summarised. Full article

Background: Isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) are enzymes that catalyze the oxidative decarboxylation of isocitrate to alpha-ketoglutarate (α-KG), which is essential for many metabolic processes, including some steps in DNA repair. In tumors, notably in gliomas, IDH1 and IDH2 are frequently mutated. The mutation found in different cancers is functionally active, causing, instead of α-KG, the formation of 2-hydroxyglutarate (2-HG), which inhibits α-KG-dependent enzymes. Gliomas harboring mutated IDH1/2 show a better prognosis than IDH1 wild-type (wt) tumors of the same grade, which might result from the inhibition of DNA repair functions. A DNA repair enzyme dependent on α-KG is alkB homolog 2 (ALKBH2), which removes several lesions from DNA. These findings prompted us to investigate the response of glioma cells to artesunate (ART), a plant ingredient with genotoxic and anticancer activity currently used in several trials. Materials and Methods: We used isogenic glioblastoma cell lines that express IDH1 wild-type or, based on a TET-inducible system, the IDH1 mutant (mt) protein, and treated them with increasing doses of artesunate. We also treated glioblastoma cells with 2-HG, generated ALKBH2 knockout cells, and checked their sensitivity to the cytotoxic effects of artesunate. Results: We show that the cell-killing effect of ART is enhanced if the IDH1 mutant (R132H) is expressed in glioblastoma cells. Further, we show that 2-HG imitates the effect of IDH1mt as 2-HG ameliorates the cytotoxicity of ART. Finally, we demonstrate that the knockout of ALKBH2 causes the sensitization of glioblastoma cells to ART. Conclusions: The data indicate that ALKBH2 protects against the anticancer effect of ART, and the mutation of IDH1/2 commonly occurring in low-grade gliomas sensitizes to ART via an ALKBH2-dependent mechanism. The data support the use of ART in the therapy of IDH1/2-mutated cancers both in combination with chemotherapy and adjuvant treatment. Full article

Background: The occurrence (incidence or prevalence) of delirium in brain tumors is unknown, yet delirium is associated with increased morbidity and mortality and worse quality of life. We conducted a systematic review and meta-analysis to determine the occurrence of delirium in hospitalized patients with brain tumors. Methods: PubMed, Scopus, and Web of Science were systematically searched for papers from 1 January 1999 to 12 July 2024, including references from texts. Cross-sectional, prospective, and other cohort study designs were included, and individual case reports, case series, editorials, and reviews were excluded. The included papers were scored using a validated sensitivity analysis tool and tested for quality and bias using funnel plots and Egger’s test. We used random effects models for the summary estimates. We performed subgroup analyses by tumor type, tumor location, delirium subtype, and length of stay. Results: Of the 452 studies screened, 27 were included, representing 35,958 patients. The overall occurrence of delirium was 0.17 (95% CI [0.11–0.24]). Delirium occurrence in patients with low-grade gliomas, high-grade gliomas, and brain metastases was 0.10 [0.06–0.16], 0.21 [0.10–0.40], and 0.31 [0.16–0.50], respectively. Compared to the occipital lobe, there was a higher occurrence of delirium for tumors in the frontal (RR 3.08 [1.35–8.22]) and temporal lobes (RR 2.88 [1.22–7.93]). The patients were more likely to have hypoactive (RR 1.61 [1.30; 1.98]) than hyperactive delirium. Delirium was associated with 4.62 additional hospitalized days compared to those without delirium (CI [3.23–6.01]). Discussion: We confirmed high occurrence rates of delirium in patients hospitalized with brain tumors. Patients with brain metastases had a higher occurrence of delirium compared to patients with gliomas, and delirium occurrence rates were higher in patients with frontotemporal tumors. Delirium occurrence rates in the literature are very heterogeneous and point toward a need for tailored assessments in patients with brain tumors. Full article

Background: The arcuate fasciculus (AF) is a critical white matter (WM) tract that connects key cortical language-processing regions, including the so-called Broca’s and Wernicke’s areas. The aim of the present study was to quantitatively assess its radiological–anatomical–morphometric modifications according to different brain tumor histotypes. Methods: A retrospective multicentric Italian study was conducted. AF reconstructions were calculated for both hemispheres for each patient diagnosed with glioblastoma (GBM), low-grade glioma (LGG), brain metastasis, and meningioma using Elements Fibertracking 2.0 software (Brainlab AG, Munich, Germany). A 3D object of each fascicle was evaluated for its volume, average fractional anisotropy (FA), and length. The cerebral healthy hemisphere was compared to the pathological contralateral in different tumor histotypes. Results: In total, 1294 patients were evaluated. A total of 156 met the inclusion criteria. We found a significant difference between healthy hemisphere and the contralateral for AF mean length and volume (p = 0.01 and p < 0.001, respectively). Considering separately the different tumor histotypes, the GBM subgroup (98, 63%) confirmed the results for mean FA and volume (p-value < 0.001); LGG patients (26, 17%) showed no significant difference between healthy and pathological hemisphere for AF mean length, mean FA, and volume (p-value 0.5, p-value 0.3, p-value <0.1, respectively). In patients affected by brain metastasis (18, 12%), Student’s t-test showed a significant difference for FA (p-value 0.003). No differences were found in patients affected by meningiomas (14, 9%) (14). Conclusions: Thorough knowledge of the microscopic anatomy and function of the arcuate fasciculus, as well as the pattern of growth of the different brain tumor histotypes, along with a careful preoperative neuroradiological assessment are mandatory to plan a tailored surgical strategy and perform a safe and effective surgical technique. The AF could be displaced and infiltrated/destructed by the solid component and peritumoral edema, respectively, of GBM. LGG shows a prevalent infiltrative pattern. Metastases account for AF dislocation due to peritumoral edema. Meningiomas do not affect WM anatomy. Full article

Background/Objectives: This study evaluates intratumoral susceptibility signals (ITSS) as imaging markers for glioma grade prediction and their association with molecular and histopathologic features, in the context of the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5). Methods: We retrospectively analyzed patients with adult diffuse gliomas who underwent pretreatment magnetic resonance imaging. ITSS were semiquantitatively graded by two radiologists: grade 0 (no signal), grade 1 (1–5), grade 2 (6–10), and grade 3 (≥11). Relative cerebral blood volume (rCBV) and tumor volume were also obtained. Histopathologic features included tumor grade, Ki-67, mitotic count, necrosis, microvascular proliferation, and molecular alterations (isocitrate dehydrogenase [IDH], 1p/19q, cyclin-dependent kinase inhibitors 2A and 2B [CDKN2A/B], and p53). Regression models predicted tumor grade (low: 1–2, high: 3–4) using ITSS, tumor volume, and rCBV. ROC curves and diagnostic performance metrics were analyzed. Results: 99 patients were included. ITSS grading correlated with rCBV, tumor volume, mitotic count, Ki-67, and tumor grade (p < 0.001). ITSS grades 0–1 were associated with oligodendrogliomas and astrocytomas (p < 0.001), IDH mutations (p < 0.001), and 1p/19q co-deletions (p = 0.01). ITSS grades 2–3 were linked to glioblastomas (p < 0.001), necrosis (p < 0.001), microvascular proliferation (p < 0.001), and CDKN2A/B homozygous deletions (p = 0.02). Models combining ITSS with rCBV and volume showed AUC of 0.94 and 0.96 (p < 0.001), outperforming univariate models. Conclusions: Semiquantitative ITSS grading correlates with key histopathologic and molecular glioma features. Combined with perfusion and volumetric parameters, ITSS enhance non-invasive glioma grading, in alignment with WHO CNS5. Full article

Background and Objectives: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. Materials and Methods: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T₁WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. Results: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. Conclusions: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas. Full article

Integrin αV (IαV) and the urokinase-type plasminogen activator receptor (uPAR) are key mediators of tumor malignancy in Glioblastoma. This study aims to characterize IαV/uPAR interaction in GBM and investigate the role played by glycans in this scenario. Protein expression and interaction were confirmed via confocal microscopy and co-immunoprecipitation. The role of N-glycosylation was evaluated using Swainsonine (SW) and PNGase F. IαV glycoproteomic analysis was performed by mass spectrometry. Sialic acids and glycan structures in IαV/uPAR interaction were tested using neuraminidase A (NeuA) and lectin interference assays, respectively. Protein expression and their interaction were detected in GBM cells, but not in low-grade glioma cells, even in cells transfected to overexpress uPAR. SW, PNGase, and NeuA treatments significantly reduced IαV/uPAR interaction. Also, lectin interference assays indicated that β1-6 branched glycans play a crucial role in this interaction. Analysis of the IαV glycosylation profile revealed the presence of complex and hybrid N-glycans in GBM, while only oligomannose N-glycans were identified in low-grade glioma. N-glycosylation inhibition and sialic acid removal reduced AKT phosphorylation. Our findings demonstrate, for the first time, the interaction between IαV and uPAR in GBM cells, highlighting the essential role of N-glycosylation, particularly β1-6 branched glycans and sialic acids. Full article

Objectives: To assess amide proton transfer weighted (APTw) MR imaging capabilities in differentiating high-grade glial tumors across alpha-thalassemia/mental retardation X-linked (ATRX) expression, tumor-suppressor protein p53 expression (p53), O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, isocitrate dehydrogenase (IDH) status, and proliferation marker Ki-67 (Ki-67 index) as a preoperative diagnostic aid. Material & Methods: A total of 42 high-grade glioma WHO grade 4 (HGG) patients were evaluated prospectively (30 males and 12 females). All patients were examined using conventional MRI, including the following: T1w-MPRAGE pre- and post-contrast administration, conventional T2w and 3D FLAIR, and APTw imaging with a 3T MR scanner. Receiver operating characteristic (ROC) curves were calculated for the APTw% mean, median, and max signal for the different molecular biomarkers. A logistic regression model was constructed for combined mean and median APTw% signals for p53 expression. Results: The whole-tumor max APTw% signal could significantly differentiate MGMTp from non-MGMTp HGG, p = 0.035. A cutoff of 4.28% max APTw% signal yielded AUC (area under the curve) = 0.702, with 70.6% sensitivity and 66.7% specificity. The mean/median APTw% signals differed significantly in p53 normal versus p53-overexpressed HGG s: 1.81%/1.83% vs. 1.15%/1.18%, p = 0.002/0.006, respectively. Cutoffs of 1.25%/1.33% for the mean/median APTw% signals yielded AUCs of 0.786/0.757, sensitivities of 76.9%/76.9%, and specificities of 50%/66.2%, p = 0.002/0.006, respectively. A logistic regression model with a combined mean and median APTw% signal for p53 status yielded an AUC = 0.788 and 76.9% sensitivity and 66.2% specificity. ATRX-, IDH- wild type (wt) vs. mutation (mut), and the level of Ki-67 did not differ significantly, but trends were found: IDH-wt and low Ki-67 showed higher mean/median/max APTw% signals vs. IDH-mut and high Ki-67, respectively. ATRX-wt vs. mutation showed higher mean and median APTw% signals but lower max APTw% signal. Conclusions: APTw imaging can potentially be a useful marker for the stratification of p53 expression and MGMT status in high-grade glioma in the preoperative setting and potentially aid surgical decision-making. Full article

Accurate glioma categorization using magnetic resonance (MR) imaging is critical for optimal treatment planning. However, the uneven and diffuse nature of glioma borders makes manual classification difficult and time-consuming. To address these limitations, we provide a unique strategy that combines image decomposition and local texture feature extraction to improve classification precision. The procedure starts with a Gaussian filter (GF) to smooth and reduce noise in MR images, followed by non-subsampled Laplacian Pyramid (NSLP) decomposition to capture multi-scale image information, making glioma borders more visible, TV-L1 normalization to handle intensity discrepancies, and local binary patterns (LBPs) to extract significant texture features from the processed images, which are then fed into a range of supervised machine learning classifiers, such as support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), AdaBoost, and LogitBoost, which have been trained to distinguish between low-grade (LG) and high-grade (HG) gliomas. According to experimental findings, our proposed approach consistently performs better than the state-of-the-art glioma classification techniques, with a higher degree of accuracy in differentiating LG and HG gliomas. This method has the potential to significantly increase diagnostic precision, enabling doctors to make better-informed and efficient treatment choices. Full article

Glioblastoma, classified as a grade IV astrocytoma, is an aggressive and malignant primary brain tumor with no known cure. Despite the implementation of standard medical and surgical treatment protocols, the disease often progresses with unsatisfactory outcomes. This study aimed to evaluate the cytotoxic, proapoptotic, and antimetastatic effects of anti-angiogenic monoclonal antibody bevacizumab combined with the sphingosine-1-phosphate receptor modulator fingolimod on rat glioma C6 cells. The cytotoxicity of bevacizumab and fingolimod was evaluated using the MTT assay. Proapoptotic activity was assessed through flow cytometric analyses, including Annexin V–FITC staining, caspase 3/7 activation, and mitochondrial membrane potential measurements. Morphological changes were examined using confocal microscopy. Antimetastatic effects were evaluated via anti-migration and colony formation assays. The combination of bevacizumab and fingolimod exhibited antiproliferative, cytotoxic, proapoptotic, and antimetastatic effects on C6 glioma cells at low IC₅₀ concentrations. Based on growth inhibitory, proapoptotic, and antimetastatic activities on C6 glioma cells, the combination of bevacizumab and fingolimod demonstrates significant growth-inhibitory, proapoptotic, and antimetastatic activities against C6 glioma cells, suggesting its potential as a promising pharmacotherapeutic approach for the treatment of glioblastoma. Full article

Pediatric gliomas encompass the most common brain tumor in children and are subdivided into pediatric low-grade gliomas (pLGGs) and pediatric high-grade gliomas (pHGGs). The era of molecular diagnosis has shifted the treatment paradigms and management of these patients. RAS/MAPK pathway alterations serve as the driver in the majority of pLGGs, a subset of pHGG and NF1-related plexiform neurofibromas (PNs). The role of small molecule inhibitors in the treatment of these tumors has evolved in the past decade, facilitated through multiple clinical trials and moving into earlier stages of treatment. Although these developments hold promise, questions remain regarding targeted therapy, the long-term toxicities, the duration of treatment and the potential effects on the natural history of the tumor behavior. Full article

Background: Glioma is the most common type of malignant brain tumor. Temozolomide (TMZ) is a limited systematic treatment option for glioma, including low-grade glioma (LGG) and glioblastoma (GBM). However, not all patients benefit from TMZ and some develop resistance to it. MGMT methylation has been used to identify patients who may benefit from TMZ, but it is not effective in all cases. Objectives: There is an urgent need for new biomarkers to predict the survival of patients who receive TMZ. Methods: We utilized a recently developed method called REdiscoverTE to precisely measure the expression of transposable elements (TE). We performed Cox regression analysis to assess the predictive ability for prognosis and conducted a series of correlation studies to uncover potential mechanisms. Results: We identified three TEs, LTR81B, LTR27B, and MER39B, that were strongly predictive of longer survival in glioma patients receiving chemotherapy. We discovered that the expression of these TEs was positively associated with immune cells that enhance the immune system and negatively associated with immune cells suppressing the immune response, as well as molecules that control immune checkpoints. These three TEs were also found to predict better survival in patients receiving immunotherapy. Conclusions: In conclusion, we demonstrate that the expression of TEs can serve as a novel biomarker for the overall survival of glioma patients who receive TMZ chemotherapy or immunotherapy. Full article