Search Results (73)

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology. Full article

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

Background: IDH1-mutant oligodendroglioma and astrocytoma differ not only in growth rate but also in growth pattern. Oligodendrogliomas tend to infiltrate white matter tracts, whereas astrocytomas more often displace them. Such difference could lead to different cognitive outcomes. This study examined differences in executive functioning before and up to one year after surgery between patients with IDH1-mutant astrocytoma and oligodendroglioma. Methods: Patients with WHO grade 2–3 IDH1-mutant oligodendroglioma (1p19q-codeleted) or astrocytoma were included. Cognition was assessed preoperatively, and at 3 and 12 months postoperatively using standardized computerized and paper-and-pencil tests. Groups were compared on demographics, tumor characteristics, surgical modality, extent of resection, adjuvant treatment, and baseline cognition. Longitudinal mixed models were performed to investigate differences in performances over time for the total sample and stratified by surgical approach (awake vs. asleep). Results: 162 patients (67 oligodendroglioma, 95 astrocytoma) were included. Oligodendroglioma patients were older, with more frontal and fewer temporal tumors. Oligodendroglioma patients showed a greater impairment prevalence on a measure of inhibition before surgery. In the awake surgery group, no longitudinal differences were found between diagnoses. In the asleep surgery group, astrocytoma patients remained stable while oligodendroglioma patients declined on a measure of cognitive flexibility, with performance at 3 and 12 months significantly lower than at baseline. Conclusions: Specific aspects of executive functioning in IDH1-mutant gliomas may differ by subtype. Oligodendroglioma patients showed postoperative decline in cognitive flexibility that did not recover to baseline level, particularly in case of surgery under general anesthesia. These results highlight the potential relevance of tumor subtype and surgical approach in limiting cognitive risks after glioma surgery. Full article

The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology. Full article

Background and Clinical Significance: Tumefactive demyelinating lesions (TDLs) are large demyelinating lesions that mimic intracranial tumors, posing a diagnostic challenge in both clinical presentation and conventional imaging. Distinguishing TDLs from central nervous system tumors can be challenging due to their similar imaging appearances. Specific magnetic resonance imaging (MRI) features such as open-ring contrast enhancement, mild mass effect, lack of cortical involvement, and rapid responsiveness to corticosteroids favor a demyelinating etiology of the lesion. This report presents a case of a tumefactive demyelination lesion showing a T2/fluid-attenuated inversion recovery (FLAIR) mismatch sign suggestive of a low-grade astrocytoma, focusing on imaging findings, therapeutic response, and diagnostic considerations. Case Description: A 63-year-old woman presented with headache, progressive speech impairment, and difficulty swallowing. MRI revealed a large lesion in the left frontal lobe with a T2/FLAIR mismatch sign, which initially suggested a low-grade astrocytoma. Additionally, the lesion was hypodense on noncontrast computed tomography (CT), did not show open-ring enhancement, and only had mild mass effect with perifocal edema. Given these conflicting imaging findings, a biopsy was considered; however, the patient declined the procedure and agreed to a follow-up. Corticosteroid therapy was initiated to reduce swelling, resulting in a significant reduction in the lesion within two weeks. A follow-up MRI confirmed near-complete regression of the lesion after two months. Conclusions: While a T2/FLAIR mismatch sign correlates with isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted astrocytoma, the dynamic radiological and clinical response to corticosteroids was more indicative of demyelination. This case highlights the importance of considering TDLs in the differential diagnosis of tumor-like brain lesions to avoid unnecessary invasive interventions like biopsy or surgical removal. Full article

Maximal safe surgical resection is a foundational principle in brain tumor surgery. To date, many intraoperative modalities have been developed to help facilitate the identification of brain tumor versus normal brain tissue so that surgical resection is maximized but limited to the boundaries of the tumor for preservation of neurological function. Of note, Raman spectroscopy has been adapted into one of these modalities because of its ability to provide rapid, non-destructive, label-free intraoperative evaluation of tumor borders and molecular classifications and help guide surgical decision-making in real time. In this review, we performed a literature review of the landmark studies incorporating Raman spectroscopy into neurosurgical care to highlight its current applications and limitations. In this modern day, Raman spectroscopy is able to detect tumor cells intraoperatively for primary glial neoplasms, meningiomas, and brain metastases with greater than 90% accuracy. For glioma surgery, a major recent advancement is the ability to detect different mutations intraoperatively, specifically IDH, 1p19q co-deletion, and ATRX, given their implications on survival and how much extent of resection should be ideally achieved. With recent advancements in artificial intelligence and their integration into stimulated Raman histology, many of these tasks can be completed in as fast as ~10 s and on average 2–3 min. Despite the incorporation of artificial intelligence, spectral data can still be heavily influenced by background noise, and its preprocessing has significant variability across platforms, which can impact the accuracy of results. Overall, Raman spectroscopy has significantly changed the intraoperative workflow of brain tumor surgery, and this review highlights the capabilities that neurosurgeons can currently take advantage of in their practice, the existing data to support it, and the areas that researchers can further optimize to improve accuracy and patient outcomes. Full article

The abstract has been submitted for presentation to the AANS 2026 meeting being held in San Antonio, TX, USA. Introduction: Oligodendrogliomas are an uncommon subset of gliomas that are molecularly defined by 1p/19q codeletion in the setting of an isocitrate dehydrogenase (IDH) 1/2 mutation. Standard-of-care management involves maximal safe resection followed by adjuvant chemoradiation with procarbazine, lomustine, and vincristine (PCV). Although PCV confers a durable survival advantage, treatment-limiting toxicity is common and often necessitates discontinuation. IDH inhibitors such as vorasidenib have demonstrated promising efficacy and more favorable tolerability profiles, but a paucity of comparative data across therapeutic classes limits optimal treatment decision-making. Methods: A systematic search was conducted through to 7 March 2025 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Eligible studies included adult patients (≥18 years) with IDH-mutant, 1p/19q-codeleted oligodendrogliomas treated with PCV chemotherapy or IDH inhibitors and with a minimum follow-up of 12 months. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and grade ≥ 3 adverse events (AEs) that led to treatment discontinuation. Results: Twenty-eight studies met the inclusion criteria, with a total of 406 patients. All 406 patients carried a confirmed diagnosis of oligodendroglioma. For mixed-histology cohorts, only oligodendroglioma-specific data were extracted and analyzed. Among PCV cohorts, median PFS ranged from 24.3 months to 8.4 years and median OS was reported up to 14.7 years in long-term follow-up from RTOG 9402 and EORTC 26951. Grade ≥ 3 AEs resulted in treatment discontinuation in 65–70% of patients, primarily due to hematologic or neurologic events. In comparison, vorasidenib achieved a median PFS of 27.7 months in the phase III INDIGO trial (HR 0.39; 95% CI 0.27–0.56; p < 0.001), with median OS not yet reached at 14.2 months of follow-up. Grade ≥ 3 AEs occurred in 22.8% of patients and led to treatment discontinuation in only 1–3%, primarily due to asymptomatic transaminitis. Early real-world data from expanded-access programs similarly support these tolerability findings. Conclusions: While PCV chemotherapy remains the standard-of-care systemic therapy for oligodendroglioma supported by mature survival data, IDH inhibitors represent a mechanistically targeted alternative with encouraging early-phase outcomes and a significantly improved safety profile. Direct comparison across these regimens is constrained by differences in study design and limited long-term OS data for IDH inhibitors. Prospective head-to-head trials are essential for defining the optimal therapeutic sequence in this evolving treatment landscape. In the interim, we provide a recommend approach for current use. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Background: The androgen receptor (AR) is a ligand-dependent transcription factor of the nuclear steroid receptor superfamily, implicated in the pathogenesis of various solid tumors. The AR gene, located on chromosome Xq11–12, is accompanied by several X-linked genes that modulate AR expression and function, including FLNA, UXT, and members of the melanoma antigen gene (MAGE) family (MAGEA1, MAGEA11, MAGEC1, MAGEC2). While the AR has been investigated in multiple tumor types, its role in adult-type diffuse gliomas remains largely unexplored. Here, we characterized AR protein expression and the promoter methylation status of the AR and associated regulatory genes in adult-type diffuse gliomas. Methods: A retrospective analysis was conducted on 50 patients with adult-type diffuse gliomas, including IDH-mutant gliomas (grades 2–4) and IDH-wildtype glioblastomas (GBMs), classified according to the 2021 WHO criteria. AR nuclear expression was assessed by immunohistochemistry (IHC). Methylation-specific PCR and quantitative DNA methylation analyses were employed to evaluate promoter methylation of the AR and selected co-regulatory genes. Results: AR nuclear positivity correlated significantly with male sex (p = 0.04) and higher tumor grade, with the highest expression in IDH-wildtype GBMs (p = 0.04). In IDH-mutant gliomas, AR immunoreactivity was more prevalent in astrocytomas than in 1p/19q codeleted oligodendrogliomas (p = 0.02). AR expression was associated with unmethylated MGMT promoter status (p = 0.02). DNA methylation analysis revealed AR gene hypomethylation in tumors displaying nuclear AR positivity and in IDH-wildtype GBMs (Kruskal–Wallis p < 0.05). Additionally, methylation patterns of AR co-regulators located on the X chromosome suggest epigenetic regulation of AR signaling in gliomas. Conclusions: The findings reveal distinct AR pathway activation patterns in adult-type diffuse gliomas, particularly IDH-wildtype GBMs, suggesting that further exploration of antiandrogen therapies is warranted. Full article

Background/Objectives: Methionine (MET) positron emission tomography (PET) and cerebral blood volume (CBV) imaging provide complementary glioma assessment. This study compared MET and CBV across glioma subtypes defined by the 2021 World Health Organization Classification. Methods: This retrospective study enrolled 106 patients (mean age 41.9 ± 12.4 years; 57 males) with MRI non-contrast-enhanced gliomas: 21 glioblastoma, isocitrate dehydrogenase (IDH)-wildtype (G); 50 astrocytoma, IDH-mutant (A); and 35 oligodendrogliomas, IDH-mutant, and 1p/19q-codeleted (O). Relative CBVs (rCBVs) were measured in VOI-T2 and VOI-MET, and the MET tumor-to-normal (T/N) ratio was calculated. Results: MET and rCBV were significantly correlated (r = 0.5, p < 0.001); rCBV was higher in MET-positive tumors and predicted MET accumulation (area under the curve [AUC] = 0.72, cutoff = 2.99). In VOI-T2, rCBV and MET T/N ratio were the highest in G and lowest in A (p < 0.001). Receiver operating characteristic analyses showed no overall significant difference between MET and rCBV for differentiating G/A/O, but rCBV trended toward higher AUC values in key distinctions, such as G (0.736 vs. 0.612) or grade 4 (0.718 vs. 0.617). The increase in rCBV within the MET-positive region (VOI-MET/VOI-T2 rCBV ratio) was significantly higher in A (119.8%, p = 0.002) than in the other groups (p = 0.01). Conclusions: rCBV differentiated glioma subtype with accuracy comparable to MET and could predict MET accumulation. However, its reliability for identifying MET-positive regions varied by subtype, being useful in A but limited in O. Recognizing these subtype-specific differences, rCBV can serve as a practical tool for evaluating non-contrast-enhanced gliomas. Full article

Background: Gliomas are among the most complex and lethal primary brain tumors, necessitating precise evaluation of both anatomical subregions and molecular alterations for effective clinical management. Methods: To find a solution to the disconnected nature of current bioimage analysis pipelines, where anatomical segmentation based on MRI and molecular biomarker prediction are done as separate tasks, we use here Molecular-Genomic and Multi-Task (MGMT-Net), a one deep learning scheme that carries out the task of the multi-modal MRI data without any conversion. MGMT-Net incorporates a novel Cross-Modality Attention Fusion (CMAF) module that dynamically integrates diverse imaging sequences and pairs them with a hybrid Transformer–Convolutional Neural Network (CNN) encoder to capture both global context and local anatomical detail. This architecture supports dual-task decoders, enabling concurrent voxel-wise tumor delineation and subject-level classification of key genomic markers, including the IDH gene mutation, the 1p/19q co-deletion, and the TERT gene promoter mutation. Results: Extensive validation on the Brain Tumor Segmentation (BraTS 2024) dataset and the combined Cancer Genome Atlas/Erasmus Glioma Database (TCGA/EGD) datasets demonstrated high segmentation accuracy and robust biomarker classification performance, with strong generalizability across external institutional cohorts. Ablation studies further confirmed the importance of each architectural component in achieving overall robustness. Conclusions: MGMT-Net presents a scalable and clinically relevant solution that bridges radiological imaging and genomic insights, potentially reducing diagnostic latency and enhancing precision in neuro-oncology decision-making. By integrating spatial and genetic analysis within a single model, this work represents a significant step toward comprehensive, AI-driven glioma assessment. Full article

Over recent decades, significant advancements have been made in the treatment and imaging of gliomas. Conventional imaging techniques, such as MRI and CT, play critical roles in glioma diagnosis and treatment but often fail to distinguish between tumor pseudoprogression (Psp) and radiation necrosis (RN) versus true progression (TP). Emerging fields like radiomics and radiogenomics are addressing these challenges by extracting quantitative features from medical images and correlating them with genomic data, respectively. This article will discuss several studies that show how radiomic features (RFs) can aid in better patient stratification and prognosis. Radiogenomics, particularly in predicting biomarkers such as MGMT promoter methylation and 1p/19q codeletion, shows potential in non-invasive diagnostics. Radiomics also offers tools for predicting tumor recurrence (rBT), essential for treatment management. Further research is needed to standardize these methods and integrate them into clinical practice. This review underscores radiomics and radiogenomics’ potential to revolutionize glioma management, marking a significant shift towards precision neuro-oncology. 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

Oligodendroglioma is a central nervous system tumor defined by IDH1/2 mutations and 1p/19q co-deletion. Current management involves maximal resection followed by radiotherapy/chemotherapy, yielding a 20-year survival rate of 37% for grade 3 tumors according to the WHO 2021 classification. As these tumors primarily affect young to middle-aged patients, novel therapies are urgently needed to improve outcomes. Immunotherapy has revolutionized tumor treatment by modulating immune responses. However, its application in oligodendrogliomas faces two major hurdles, including the immunosuppressive tumor microenvironment (TME) and the blood–brain barrier’s restrictive properties. This review first examines oligodendroglioma’s molecular alterations to refine diagnosis and guide targeted therapies. Next, we focus on the oligodendroglioma TME to evaluate emerging immunotherapies, including oncolytic viruses, immune checkpoint blockade, chimeric antigen receptor (CAR) T-cell therapy, and cancer vaccines. Finally, we discuss current challenges and future directions to overcome therapeutic limitations and advance treatment strategies. Full article

Objectives: The objective of this study is to explore the potential variations in metabolic activity across gliomas originating from distinct cortical regions, as assessed by O-(2-¹⁸F-fluoroethyl)-L-tyrosine positron emission tomography (¹⁸F-FET PET). Also, this study seeks to elucidate whether these metabolic disparities correlate with the molecular characteristics and clinical prognoses of the tumors. Specifically, this research aims to determine whether variations in ¹⁸F-FET PET uptake are indicative of underlying genetic or biochemical differences that could influence patients’ outcomes. Methods: The researchers retrospectively included 107 patients diagnosed with gliomas from neocortex and mesocortex, all of whom underwent hybrid PET/MR examinations, including ¹⁸F-FET PET and diffusion weighted imaging (DWI), prior to surgery. The mean and maximum tumor-to-background ratio (TBR) and apparent diffusion coefficient (ADC) values were calculated based on whole tumor volume segmentations. Comparisons of TBR, ADC values, and survival outcomes were performed to determine statistical differences between groups. Results: Among glioblastomas (GBMs, WHO grade 4) originating from the two cortical regions, there was a significant difference in the human Telomerase Reverse Transcriptase (TERT) promoter mutation rate, while no difference was observed in O⁶-Methylguanine-DNA Methyltransferase (MGMT) promoter methylation status. For WHO grade 3 gliomas, significant differences were found in the TERT promoter mutation rate and the proportion of 1p/19q co-deletion between the two cortical regions, whereas no difference was noted in MGMT methylation status. For WHO grade 2 gliomas, no molecular phenotypic differences were observed between the two cortical regions. In terms of survival, only GBMs originating from the mesocortex demonstrated significantly longer survival compared to those from the neocortex, while no statistically significant differences were found in survival for the other two groups. Conclusions: Gliomas originating from different cortical regions exhibit variations in metabolic activity, molecular phenotypes, and clinical outcomes. Full article