Epidemiology and risk factors for adult gliomas died by respiratory diseases during the COVID-19 pandemic: a population-based study.

BACKGROUND: The research on epidemiology of gliomas died of respiratory diseases (RDs) is very scarce. The study aimed to explore the epidemiology and risk factors for adult gliomas death from respiratory diseases during the COVID-19 pandemic. METHODS: Adult gliomas patients (age ≥ 18 years) diagnosed between 2020 and 2021 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching analysis was used to reduce confounding bias between gliomas died of respiratory diseases and died of gliomas directly. The Cox proportional hazards regression model and Kaplan-Meier (K-M) survival curves were used for survival analyses in the matched groups. Logistic regression analyses were conducted to identify risk factors for dying of respiratory diseases in the entire population. RESULTS: Among 9315 eligible adult gliomas enrolled in the study, 39.4% died from gliomas, 1.0% from respiratory diseases, and 61.4% survived. Gliomas who died from respiratory diseases had a trend towards a higher risk of death (HR = 1.35, P = 0.031). Surgery did not increase the all-cause mortality risk (HR = 0.86, P = 0.327). The K-M survival curves suggested a worse prognosis for dying from respiratory diseases. Those who died from RDs had a shortened median survival (median 3 months) compared with those who died from gliomas directly (median 5 months). Multivariable logistic regression models indicated that those aged ≥ 65 years, with median household income < 75,000$/year, and not receiving surgery had a higher risk of dying from RDs. CONCLUSIONS: RDs have become a crucial cause of death for gliomas. Those with advanced age and lower median household income have a higher risk of dying from respiratory diseases. Surgical treatment has been found to be safe for glioma patients and has been shown to reduce the risk of glioma patients dying from respiratory diseases. The study provides valuable insights for the perioperative management of gliomas patients in the post-pandemic era.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

Pilomyxoid Astrocytoma Presenting With Developmental Regression: A Case Report.

Pilomyxoid astrocytoma (PMA) is a subtype of pilocytic astrocytoma (PA). PMA tends to exhibit a more aggressive course compared to PA. We present a case of a two-year-old male with a PMA in the suprasellar region who presented with developmental regression, loss of previously attained milestones such as the ability to hold his neck, walk, and talk, along with hypotonia in all four limbs. Serum cortisol and thyroid-stimulating hormone (TSH) levels were measured to rule out endocrine disturbances and were within normal limits. Magnetic resonance imaging (MRI) of the brain showed a solid lesion in the suprasellar region, extending into the pituitary and interpeduncular fossae, compressing the pituitary gland, and effacing the third ventricle, causing cerebrospinal fluid (CSF) flow obstruction and lateral ventricle dilation. The tumor appears hypointense on T1 and hyperintense on T2, with fluid-attenuated inversion recovery (FLAIR), peripheral contrast enhancement, and no calcification, consistent with PMA. The CSF analysis was negative for malignant cells. Histopathological examination revealed monomorphous bipolar and spindle cells in an angiocentric pattern with a myxoid background, without rosenthal fibers, mitoses, or eosinophilic granular bodies, consistent with PMA but not seen in PA. Immunohistochemistry showed strong positivity for glial fibrillary acidic protein (GFAP) and S100, with a Ki-67 index of 3-4%, indicating a low-grade tumor. The preferred treatment is surgical resection, but due to the tumor's deep location and potential long-term neurological effects, the parents opted against surgery. A ventriculoperitoneal shunt was placed to alleviate CSF flow, following which the child showed mild improvement in symptoms. Treatment of nonresectable astrocytomas was controversial, but gross total surgical resection offers better disease control. Chemotherapy is for patients with recurrence or where total resection of the tumor is not possible, and radiotherapy, though the long-term disease control is good, has a variable visual outcome.

Targeting ferroptosis opens new avenues in gliomas.

Gliomas are one of the most challenging tumors to treat due to their malignant phenotype, brain parenchymal infiltration, intratumoral heterogeneity, and immunosuppressive microenvironment, resulting in a high recurrence rate and dismal five-year survival rate. The current standard therapies, including maximum tumor resection, chemotherapy with temozolomide, and radiotherapy, have exhibited limited efficacy, which is caused partially by the resistance of tumor cell death. Recent studies have revealed that ferroptosis, a newly defined programmed cell death (PCD), plays a crucial role in the occurrence and progression of gliomas and significantly affects the efficacy of various treatments, representing a promising therapeutic strategy. In this review, we provide a comprehensive overview of the latest progress in ferroptosis, its involvement and regulation in the pathophysiological process of gliomas, various treatment hotspots, the existing obstacles, and future directions worth investigating. Our review sheds light on providing novel insights into manipulating ferroptosis to provide potential targets and strategies of glioma treatment.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

BACKGROUND: Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. PURPOSE: Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. METHODS: Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. RESULTS: For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively). CONCLUSIONS: We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Alterations in Astrocyte Subpopulations in Glioma and Identification of Cuproptosis-Related Genes Using Single-Cell RNA Sequencing.

Purpose: Mitochondrial metabolism is essential for energy production and the survival of brain cells, particularly in astrocytes. Cuproptosis is a newly identified form of programmed cell death that occurs due to the disruption of mitochondrial metabolism caused by excessive copper toxicity. However, the relationship between cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) and the prognosis of gliomas remains unclear. Patients and Methods: In this study, we utilized 32,293 cells obtained from three in-house single-cell RNA sequencing (scRNA-seq) datasets, along with 6,148 cells acquired from the Chinese Glioma Genome Atlas (CGGA) involving 14 glioma patients, to identify and validate the TME of gliomas. Results: Based on an analysis of 32,293 single cells, we investigated intra-tumor heterogeneity, intercellular communication, and astrocyte differentiation trajectories in gliomas. Our findings revealed that the TGFß signaling pathway exhibited a higher relative strength in astrocyte subpopulations. Additionally, we identified a novel three-gene signature (CDKN2A, SOX2, and MPC1) was identified for prognostic prediction. Furthermore, glioma patients with a high-risk score demonstrated poorer overall survival (OS) compared to those with a low-risk score in both training and testing datasets (P training set < 0.001; P test set = 0.037). Conclusion: Our study revealed the prognostic value of the CRGs in astrocytes exhibiting tumor immunosuppressive characteristics in glioma. We established a novel three-gene prognostic model that offers new insights into the prognosis and treatment strategies for gliomas.

Multifaceted bioinformatic analysis of m6A-related ferroptosis and its link with gene signatures and tumour-infiltrating immune cells in gliomas.

Whether N6-Methyladenosine (m6A)- and ferroptosis-related genes act on immune responses to regulate glioma progression remains unanswered. Data of glioma and corresponding normal brain tissues were fetched from the TCGA database and GTEx. Differentially expressed genes (DEGs) were identified for GO and KEGG enrichment analyses. The FerrDb database was based to yield ferroptosis-related DEGs. Hub genes were then screened out using the cytoHubba database and validated in clinical samples. Immune cells infiltrating into the glioma tissues were analysed using the CIBERSORT R script. The association of gene signature underlying the m6A-related ferroptosis with tumour-infiltrating immune cells and immune checkpoints in low-grade gliomas was analysed. Of 6298 DEGs enriched in mRNA modifications, 144 were ferroptosis-related; NFE2L2 and METTL16 showed the strongest positive correlation. METTL16 knockdown inhibited the migrative and invasive abilities of glioma cells and induced ferroptosis in vitro. NFE2L2 was enriched in the anti-m6A antibody. Moreover, METTL16 knockdown reduced the mRNA stability and level of NFE2L2 (both p < 0.05). Proportions of CD8+ T lymphocytes, activated mast cells and M2 macrophages differed between low-grade gliomas and normal tissues. METTL16 expression was negatively correlated with CD8+ T lymphocytes, while that of NFE2L2 was positively correlated with M2 macrophages and immune checkpoints in low-grade gliomas. Gene signatures involved in the m6A-related ferroptosis in gliomas were identified via bioinformatic analyses. NFE2L2 interacted with METTL16 to regulate the immune response in low-grade gliomas, and both molecules may be novel therapeutic targets for gliomas.

Artificial intelligence and advanced MRI techniques: A comprehensive analysis of diffuse gliomas.

INTRODUCTION: The complexity of diffuse gliomas relies on advanced imaging techniques like MRI to understand their heterogeneity. Utilizing the UCSF-PDGM dataset, this study harnesses MRI techniques, radiomics, and AI to analyze diffuse gliomas for optimizing patient outcomes. METHODS: The research utilized the dataset of 501 subjects with diffuse gliomas through a comprehensive MRI protocol. After performing intricate tumor segmentation, 82.800 radiomic features were extracted for each patient from nine segmentations across eight MRI sequences. These features informed neural network and XGBoost model training to predict patient outcomes and tumor grades, supplemented by SHAP analysis to pinpoint influential radiomic features. RESULTS: In our analysis of the UCSF-PDGM dataset, we observed a diverse range of WHO tumor grades and patient outcomes, discarding one corrupt MRI scan. Our segmentation method showed high accuracy when comparing automated and manual techniques. The neural network excelled in prediction of WHO tumor grades with an accuracy of 0.9500 for the necrotic tumor label. The SHAP-analysis highlighted the 3D First Order mean as one of the most influential radiomic features, with features like Original Shape Sphericity and Original Shape Elongation were notably prominent. CONCLUSION: A study using the UCSF-PDGM dataset highlighted AI and radiomics' profound impact on neuroradiology by demonstrating reliable tumor segmentation and identifying key radiomic features, despite challenges in predicting patient survival. The research emphasizes both the potential of AI in this field and the need for broader datasets of diverse MRI sequences to enhance patient outcomes. IMPLICATION FOR PRACTICE: The study underline the significant role of radiomics in improving the accuracy of tumor identification through radiomic features.

Chromosomal instability: a key driver in glioma pathogenesis and progression.

Chromosomal instability (CIN) is a pivotal factor in gliomas, contributing to their complexity, progression, and therapeutic challenges. CIN, characterized by frequent genomic alterations during mitosis, leads to genetic abnormalities and impacts cellular functions. This instability results from various factors, including replication errors and toxic compounds. While CIN's role is well documented in cancers like ovarian cancer, its implications for gliomas are increasingly recognized. CIN influences glioma progression by affecting key oncological pathways, such as tumor suppressor genes (e.g., TP53), oncogenes (e.g., EGFR), and DNA repair mechanisms. It drives tumor evolution, promotes inflammatory signaling, and affects immune interactions, potentially leading to poor clinical outcomes and treatment resistance. This review examines CIN's impact on gliomas through a narrative approach, analyzing data from PubMed/Medline, EMBASE, the Cochrane Library, and Scopus. It highlights CIN's role across glioma subtypes, from adult glioblastomas and astrocytomas to pediatric oligodendrogliomas and astrocytomas. Key findings include CIN's effect on tumor heterogeneity and its potential as a biomarker for early detection and monitoring. Emerging therapies targeting CIN, such as those modulating tumor mutation burden and DNA damage response pathways, show promise but face challenges. The review underscores the need for integrated therapeutic strategies and improved bioinformatics tools like CINdex to advance understanding and treatment of gliomas. Future research should focus on combining CIN-targeted therapies with immune modulation and personalized medicine to enhance patient outcomes.

Metabolomic profile of cerebrospinal fluid from patients with diffuse gliomas.

BACKGROUND: Diffuse gliomas are among the most common brain tumors in adults and are associated with a dismal prognosis, especially in patients with glioblastoma. To date, tumor tissue acquisition is mandatory for conclusive diagnosis and therapeutic decision-making. In this study, we aimed to identify possible diagnostic and prognostic biomarkers in cerebrospinal fluid (CSF) and blood. METHODS: During glioma surgery at our institution, CSF and blood samples were collected from patients. Subsequently, targeted metabolomics analysis was used to detect and quantify circulating metabolites. The metabolome profiles of glioma patients were compared with those of patients in a control group who had undergone neurosurgery for other entities, such as nonglial tumors or hydrocephalus, and were correlated with established glioma diagnostic molecular markers. RESULTS: In this study, a total of 30 glioma patients were included, along with a control group of 21 patients without glioma. Serum metabolomic analysis did not detect any significant differences between the groups, whereas CSF-metabolome analysis revealed increased levels of six metabolites in glioma patients. Among these, the most pronounced differences were found for the biogenic amine putrescine (p = 0.00005). p-Cresol sulfate was identified as a potential CSF marker for determining isocitrate dehydrogenase (IDH) status in glioma patients (p = 0.0037). CONCLUSION: CSF-metabolome profiling, unlike blood profiling, shows promise as a diagnostic tool for glioma patients with the potential to assign molecular subtypes. The next step will involve a larger multicenter study to validate these findings, with the ultimate objective of integrating CSF metabolomics analysis into clinical practice.

Anatomical and subcortical invasiveness in diffuse low-grade astrocytomas differ between IDH status and provide prognostic information.

Background: Diffuse astrocytomas preferentially infiltrate eloquent areas affecting the outcome. A preoperative understanding of isocitrate dehydrogenase (IDH) status may offer opportunities for specific targeted therapies impacting treatment management. The aim of this study was to analyze clinical, topographical, radiological in WHO 2 astrocytomas with different IDH status and the long-term patient's outcome. Methods: A series of confirmed WHO 2 astrocytoma patients (between 2005 and 2015) were retrospectively analyzed. MRI sequences (FLAIR) were used for tumor volume segmentation and to create a frequency map of their locations into the Montreal Neurological Institute (MNI) space. The Brain-Grid (BG) system (standardized radiological tool of intersected lines according to anatomical landmarks) was used as an overlay for infiltration analysis of each tumor. Long-term follow-up was used to perform a survival analysis. Results: Forty patients with confirmed IDH status (26 IDH-mutant, IDHm/14 IDH-wild type, IDHwt) according to WHO 2021 classification were included with a mean follow-up of 7.8 years. IDHm astrocytomas displayed a lower number of BG-voxels (P < 0.05) and were preferentially located in the anterior insular region. IDHwt group displayed a posterior insular and peritrigonal location. IDHwt group displayed a shorter OS compared with IDHm (P < 0.05), with the infiltration of 7 or more BG-voxels as an independent factor predicting a shorter OS. Conclusions: IDHm and IDHwt astrocytomas differed in preferential location, number of BG-voxels and OS at long follow-up time. The number of BG-voxels affected the OS in IDHwt was possibly reflecting higher tumor invasiveness. We encourage the systematic use of alternative observational tools, such as gradient maps and the Brain-Grid analysis, to better detect differences of tumor invasiveness in diffuse low-grade gliomas subtypes.

FAM109B plays a tumorigenic role in low-grade gliomas and is associated with tumor-associated macrophages (TAMs).

BACKGROUND: Family with sequence similarity 109, member B (FAM109B) is involved in endocytic transport and affects genetic variation in brain methylation. It is one of the important genes related to immune cell-associated diseases. In the tumor immune system, methylation can regulate tumor immunity and influence the maturation and functional response of immune cells. Whether FAM109B is involved in tumor progression and its correlation with the tumor immune microenvironment has not yet been disclosed. METHODS: A comprehensive pan-cancer analysis of FAM109B expression, prognosis, immunity, and TMB was conducted. The expression, clinical features, and prognostic value of FAM109B in low-grade gliomas (LGG) were evaluated using TCGA, CGGA, and Gravendeel databases. The expression of FAM109B was validated by qRT-PCR, immunohistochemistry (IHC), and Western blotting (WB). The relationship between FAM109B and methylation, Copy Number Variation (CNV), prognosis, immune checkpoints (ICs), and common chemotherapy drug sensitivity in LGG was explored through Cox regression, Kaplan-Meier curves, and Spearman correlation analysis. FAM109B levels and their distribution were studied using the TIMER database and single-cell analysis. The potential role of FAM109B in gliomas was further investigated through in vitro and in vivo experiments. RESULTS: FAM109B was significantly elevated in various tumor types and was associated with poor prognosis. Its expression was related to aggressive progression and poor prognosis in low-grade glioma patients, serving as an independent prognostic marker for LGG. Glioma grade was negatively correlated with FAM109B DNA promoter methylation. Immune infiltration and single-cell analysis showed significant expression of FAM109B in tumor-associated macrophages (TAMs). The expression of FAM109B was closely related to gene mutations, immune checkpoints (ICs), and chemotherapy drugs in LGG. In vitro studies showed increased FAM109B expression in LGG, closely related to cell proliferation. In vivo studies showed that mice in the sh-FAM109B group had slower tumor growth, slower weight loss, and longer survival times. CONCLUSIONS: FAM109B, as a novel prognostic biomarker for low-grade gliomas, exhibits specific overexpression in TAMs and may be a potential therapeutic target for LGG patients.

Selection for proton radiotherapy of grade 1-3 glioma patients.

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

Therapeutic inhibition of isocitrate dehydrogenase mutations in glioma and cholangiocarcinoma: new insights and promises-a narrative review.

BACKGROUND AND OBJECTIVE: The identification of mutation hot spots in the isocitrate dehydrogenase (IDH) genes is one of the most important cancer genome-wide sequencing discoveries with relevant impact in the treatment of some orphan tumors. These genes were mostly found mutated in lower-grade gliomas (LGGs), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPNs) and in cholangiocarcinoma. This aberrant genomic condition represents a therapeutic target of great interest in cancer research, especially in AML, given the limitations of currently approved therapies in this field. In this review, we investigate the role of IDH mutation and the mutant IDH (mIDH)- targeted therapies for cholangiocarcinoma and glioma. METHODS: Here, we provide an overview of the IDH mutation role and discuss its role in tumorigenesis and progression of some solid cancers, in which the therapeutic strategy can be completely changed thanks to these brand-new therapeutic options. KEY CONTENT AND FINDINGS: The encouraging early clinical data demonstrated to be a proof of concept for investigational mIDH1/2 inhibitors in tumors with a paucity of therapeutic possibilities. CONCLUSIONS: Moreover, we list the most important randomised clinical trials still active with their preliminary results.

Insular Gliomas. Experience in a Latin-American center and assessment of variables related to surgical management and prognosis.

OBJECTIVE: To describe our experience in the resection of gliomas involving the insula and analyze the variables implicated in the management and prognosis of these tumors. MATERIALS AND METHODS: This retrospective, single-center, analytical study included a cohort of 83 patients who underwent surgery for insular gliomas by the same surgeon in a third-level Argentine center, in the period between 2010-2023. We analyzed the population's demographic, clinical, and radiological features and surgical variables associated with postoperative results and prognosis using multivariate regression analysis. RESULTS: A total of 53 patients (54% males) were included with a mean follow-up of 40.7 months. Mean age at surgery was 41 years (range 21-73) and 66.1% corresponded to low-grade gliomas. Seizures were the initial symptom in the majority of cases. There was evidence of tumor extension over the insula to temporal or/and frontal lobe in 64.2% of patients. An EOR over 90% was achieved in 62.3% of cases (27% of GTR) with an average resected volume of 89.4%. Awake craniotomy was indicated in 47% of patients and iMRI was performed in 24%. Recurrence was observed in 44% of patients with a mean Progression-Free survival of 31 months (42 months in LGG and 10 months in HGG). A total of 9 patients were re-operated. By the time of 2 years, survival rates were 100% for LGG and 46% for HGG, while 4-year overall survival rates were 92% for patients with LGG and 15.4% for those with HGG. CONCLUSION: Surgery for insular gliomas is a complex task that needs to be managed with adequate preoperative and intraoperative assessment in order to achieve maximum safe resection with low morbidity for better functional and oncological outcomes. Adequate anatomical understanding, radiological analysis, awake craniotomy, cortical and subcortical mapping are paramount to pursue this aim.

Beyond surgical resection: evaluating stereotactic brachytherapy iodine-125 for low-grade gliomas: a systematic review and meta-analysis.

Stereotactic Brachytherapy Iodine-125 (SBT I-125) has been investigated by some studies for the treatment of lowgrade gliomas. We performed a meta-analysis to assess the efficacy and safety of SBT I-125 Brachytherapy for treatment of patients with Low-Grade Gliomas. PubMed, Cochrane, Web of Science, and EMBASE databases were searched for randomized and observational studies. This systematic review and meta-analysis was conducted according to the Cochrane Collaboration and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines. We used relative risk (RR) with 95% confidence intervals and random effects model to compare the effects of I-125 SBT treatment on the interest outcomes. We evaluated heterogeneity using I2 statistics; we considered heterogeneity to be significant if the p-value was less than 0.05 and I2 was higher than 35%. We performed statistical analysis using the software R (version 4.2.3). A total of 20 studies with a cohort of 988 patients with low grade gliomas who received SBT I-125 as a treatment option. The pooled analysis evidenced: (1) Complication rate of 10% (95% CI: 7-12%; I² = 60%); (2) 5-year PFS of 66% (99% CI: 45-86%; I²= 98%); (3) 10-year PFS was 66% (99% CI: 45-86%; I²= 98%); (4) Malignant transformation rate of 26% (95% CI: 8-45%; I²=0); (5) Mortality of 33% (95% CI: 15-51%; I² = 0%). Our systematic review and meta-analysis of SBT I-125 for low-grade gliomas have revealed significant concerns regarding its safety and efficacy. Despite a proportion of patients remaining progression-free, elevated rates of complications and mortality cast doubt on the intervention's reliability. Future research should prioritize long-term follow-up studies, standardized protocols, and comparative effectiveness research.

Ollier Disease, Acute Myeloid Leukemia, and Brain Glioma: IDH as the Common Denominator.

Ollier disease (OD), acute myeloid leukemia (AML), and brain glioma (BG) are three apparently completely different neoplasms in terms of histopathology, clinic, natural history, and management, but they can affect the same patient. This study aimed to identify the common molecular pathways involved in the pathogenesis of all three diseases and discuss their current and potential role as therapeutic targets. A detailed and comprehensive systematic literature review according to PRISMA guidelines on OD patients harboring BG and/or AML was made. In addition, the unique case of a patient affected by all three considered diseases has been added to our case series. Demographic, pathological, treatment, and outcome data were analyzed and discussed, mainly focusing on the molecular findings. Twenty-eight studies reported thirty-three patients affected by OD and BG, and only one study reported one patient with OD and AML, while only our patient harbored all three pathologies. The IDH R132H mutation was the only genetic alteration shared by all three pathologies and was simultaneously detected in enchondromas and brain glioma in 100% (3/3) of OD patients with BG and also in the neoplastic blood cells of the single patient hosting all three diseases. The IDH1-R132H gene mutation is the etiopathogenetic common denominator among three apparently different tumors coexisting in the same patient. The adoption of mutant-specific IDH1 inhibitor molecules could represent a potential panacea for these conditions in the era of targeted therapies. Further studies with larger clinical series are needed to confirm our results and hypothesis.

From Voxel to Gene: A Scoping Review on MRI Radiogenomics' Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas-The Promise of Virtual Biopsy?

BACKGROUND: Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. AIMS AND METHODS: This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. RESULTS: We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. CONCLUSIONS: Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies.

Advances in the Treatment of Pediatric Low-Grade Gliomas.

PURPOSE OF REVIEW: Pediatric low-grade gliomas (pLGGs) often result in significant long-term morbidities despite high overall survival rates. This review aims to consolidate the current understanding of pLGG biology and molecular features and provide an overview of current and emerging treatment strategies. RECENT FINDINGS: Surgical resection remains a primary treatment modality, supplemented by chemotherapy and radiotherapy in specific cases. However, recent advances have elucidated the molecular underpinnings of pLGGs, revealing key genetic abnormalities such as BRAF fusions and mutations and the involvement of the RAS/MAPK and mTOR pathways. Novel targeted therapies, including MEK, BRAF and pan-RAF inhibitors, have shown promise in clinical trials, demonstrating significant efficacy and manageable toxicity. Understanding of pLGGs has significantly improved, leading to more personalized treatment approaches. Targeted therapies have emerged as effective alternatives, potentially reducing long-term toxicities. Future research should focus on optimizing therapy sequences, understanding long-term impacts, and ensuring global accessibility to advanced treatments.

Significance of Nestin and CD133 as cancer stem cell markers in diffuse glioma and association with p53 expression and IDH status.

BACKGROUND: Recent evidence suggests that the tumor stem cells that are responsible for the pathogenesis of gliomas have similar properties to those of neural stem cells. We have studied two of the most consistently expressed stem cell markers in gliomas, i.e., CD133 and Nestin, and compared them with respect to p53 expression and IDH status. OBJECTIVES: To assess the level of expression of Nestin and CD133, and identify a correlation among various grades of diffuse glioma with IDH status and expression of p53. MATERIALS AND METHODS: A cross-sectional retrospective study with 102 subjects for the expression of cancer stem cell markers; CD133 and Nestin and the correlation of their expression with that of p53 and IDH1 status in adult diffuse glioma. The study was conducted in the Departments of Pathology and Neurosurgery. The expression was assessed by immunohistochemistry on formalin-fixed paraffin-embedded sections. The scoring of expression of CD133 and Nestin was adapted from Zhang et al. The scoring for p53 was adopted from Aruna et al. Results: The diffuse gliomas were graded based on WHO into grade II (30.3%), grade III (28.4%), and grade IV (41.3%). Among WHO grade IV, 59.4% were primary, and 40.4% were secondary glioblastomas. 73% of the diffuse gliomas were IDH mutant, and p53 showed an overall expression of 76.4%. The expression of CD133 and Nestin were compared with the increasing grades of diffuse gliomas, which, when plotted on ROC curves, had AUCs of 0.6806 and 0.6119, respectively. Their expression showed a positive correlation with the IDH status of the tumor. CONCLUSIONS: Cancer stem cell markers CD133 and Nestin are expressed in diffuse glioma and have a higher expression with increasing WHO grade of malignancy. These cancer stem cell markers have shown significant association with the IDH-1 mutant status of diffuse gliomas. Hence, it can be inferred that diffuse gliomas with a higher expression of CD133 and Nestin have a poorer prognosis. Further, these cancer stem cell markers may be used as therapeutic targets in the future.