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Aims: This study aims to systematically analyze the global trends in glioma methylation research using bibliometric methodologies. We focus on identifying the scholarly trajectory and key research interests, and we utilize these insights to predict future research directions within the epigenetic context of glioma. Methods: We performed a comprehensive literature search of the Web of Science Core Collection (WoSCC) to identify articles related to glioma methylation published from January 1, 2004, to December 31, 2023. The analysis included full-text publications in the English language and excluded non-research publications. Analysis and visualization were performed using GraphPad Prism, CiteSpace, and VOSviewer software. Results: The search identified 3,744 publications within the WoSCC database, including 3,124 original research articles and 620 review articles. The research output gradually increased from 2004 to 2007, followed by a significant increase after 2008, which peaked in 2022. A minor decline in publication output was noted during 2020-2021, potentially linked to the coronavirus disease 2019 pandemic. The United States and China were the leading contributors, collectively accounting for 57.85% of the total research output. The Helmholtz Association of Germany, the German Cancer Research Center (DKFZ), and the Ruprecht Karls University of Heidelberg were the most productive institutions. The Journal of Neuro-Oncology led in terms of publication volume, while Neuro-Oncology had the highest Impact Factor. The analysis of publishing authors revealed Michael Weller as the most prolific contributor. The co-citation network analysis identified David N. Louis's article as the most frequently cited. The keyword analysis revealed "temozolomide," "expression," "survival," and "DNA methylation" as the most prominent keywords, while "heterogeneity," "overall survival," and "tumor microenvironment" showed the strongest citation bursts. Conclusions: The findings of this study illustrate the increasing scholarly interest in glioma methylation, with a notable increase in research output over the past two decades. This study provides a comprehensive overview of the research landscape, highlighting the importance of temozolomide, DNA methylation, and the tumor microenvironment in glioma research. Despite its limitations, this study offers valuable insights into the current research trends and potential future directions, particularly in the realm of immunotherapy and epigenetic editing techniques.

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BACKGROUND: High-grade gliomas (HGGs) have a rapid relapse and short survival. Studies have identified many clinical characteristics and biomarkers associated with progression-free survival (PFS) and over-survival (OS). However, there has not yet a comprehensive study on survival after the first progression (SAP). METHODS: From CGGA and TCGA, 319 and 308 HGGs were confirmed as the first progression. The data on clinical characteristics and biomarkers were analyzed in accordance with OS, PFS, and SAP. RESULTS: Analysis of 319 patients from CGGA, significant predictors of improved OS/PFS/SAP were WHO grade, MGMT promoter methylation, and Ki-67 expression in univariate analysis. Further multivariate analysis showed MGMT promoter methylation and Ki-67 expression were independent predictors. However, an analysis of 308 patients from TCGA found MGMT promoter methylation is the only prognostic marker. A longer SAP was observed in patients with methylated MGMT promoter after standard chemoradiotherapy. In our data, HGGs could be divided into low, intermediate, and high-risk groups for SAP by MGMT methylation and Ki-67 expression. CONCLUSIONS: Patients with MGMT promoter methylation have a prolonger SAP after standard chemoradiotherapy. HGGs could be divided into low, intermediate, and high-risk groups for SAP according to MGMT status and Ki-67 expression.

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BACKGROUND: The treatment of elderly/ frail patients with glioblastoma is a balance between avoiding undue toxicity, while not withholding effective treatment. It remains debated, whether these patients should receive combined chemo-radiotherapy with temozolomide (RT/TMZ➜TMZ) regardless of the O6-methylguanine DNA methyltransferase gene promoter (MGMTp) methylation status. MGMT is a well-known resistance factor blunting the treatment effect of TMZ, by repairing the most genotoxic lesion. Epigenetic silencing of the MGMTp sensitizes glioblastoma to TMZ. For risk adapted treatment, it is of utmost importance to accurately identify patients, who will not benefit from TMZ treatment. METHODS: Here, we present a reanalysis of the clinical trials CE.6 and the pooled NOA-08 and Nordic trials in elderly glioblastoma patients that compared RT to RT/TMZ➜TMZ, or RT to TMZ, respectively. For 687 patients with available MGMTp methylation data, we applied a cutoff discerning truly unmethylated glioblastoma, established in a pooled analysis of four clinical trials for glioblastoma, with RT/TMZ➜TMZ treatment, using the same quantitative methylation specific MGMTp PCR assay. RESULTS: When applying this restricted cutoff to the elderly patient population, we confirmed that glioblastoma with truly unmethylated MGMTp derived no benefit from TMZ treatment. In the Nordic/NOA-08 trials RT was better than TMZ, suggesting little or no benefit from TMZ. CONCLUSION: For evidence-based treatment of glioblastoma patients validated MGMTp methylation assays should be used that accurately identify truly unmethylated patients. Respective stratified management of patients will reduce toxicity without compromising outcome and allow testing of more promising treatment options.

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AIMS: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter region is essential in evaluating the prognosis and predicting the drug response in patients with glioblastoma. In this study, we evaluated the utility of using nanopore long-read sequencing as a method for assessing methylation levels throughout the MGMT CpG-island, compared its performance to established techniques and demonstrated its clinical applicability. METHODS: We analysed 165 samples from CNS tumours, focusing on the MGMT CpG-island using nanopore sequencing. Oxford Nanopore Technologies (ONT) MinION and PromethION flow cells were employed for single sample or barcoded assays, guided by a CRISPR/Cas9 protocol, adaptive sampling or as part of a whole genome sequencing assay. Methylation data obtained through nanopore sequencing were compared to results obtained via pyrosequencing and methylation bead arrays. Hierarchical clustering was applied to nanopore sequencing data for patient stratification. RESULTS: Nanopore sequencing displayed a strong correlation (R2 = 0.91) with pyrosequencing results for the four CpGs of MGMT analysed by both methods. The MGMT-STP27 algorithm's classification was effectively reproduced using nanopore data. Unsupervised hierarchical clustering revealed distinct patterns in methylated and unmethylated samples, providing comparable survival prediction capabilities. Nanopore sequencing yielded high-confidence results in a rapid timeframe, typically within hours of sequencing, and extended the analysis to all 98 CpGs of the MGMT CpG-island. CONCLUSIONS: This study presents nanopore sequencing as a valid and efficient method for determining MGMT promotor methylation status. It offers a comprehensive view of the MGMT promoter methylation landscape, which enables the identification of potentially clinically relevant subgroups of patients. Further exploration of the clinical implications of patient stratification using nanopore sequencing of MGMT is warranted.

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Glioblastoma, IDH-wild type, the most common malignant primary central nervous system tumor, represents a formidable challenge in clinical management due to its poor prognosis and limited therapeutic responses. With an evolving understanding of its underlying biology, there is an urgent need to identify prognostic molecular groups that can be subject to targeted therapy. This study established a cohort of 124 sequential glioblastomas from a tertiary hospital and aimed to find correlations between molecular features and survival outcomes. Comprehensive molecular characterization of the cohort revealed prevalent alterations as previously described, such as TERT promoter mutations and involvement of the PI3K-Akt-mTOR, CK4/6-CDKN2A/B-RB1, and p14ARF-MDM2-MDM4-p53 pathways. MGMT promoter methylation is a significant predictor of improved overall survival, aligned with previous data. Conversely, age showed a marginal association with higher mortality. Multivariate analysis to account for the effect of MGMT promoter methylation and age showed that, in contrast to other published series, this cohort demonstrated improved survival for tumors harboring PTEN mutations, and that there was no observed difference for most other molecular alterations, including EGFR amplification, RB1 loss, or the coexistence of EGFR amplification and deletion/exon skipping (EGFRvIII). Despite limitations in sample size, this study contributes data to the molecular landscape of glioblastomas, prompting further investigations to examine these findings more closely in larger cohorts.

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Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

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Background: The study aims to explore MRI phenotypes that predict glioblastoma's (GBM) methylation status of the promoter region of MGMT gene (pMGMT) by qualitatively assessing contrast-enhanced T1-weighted intensity images. Methods: A total of 193 histologically and molecularly confirmed GBMs at the Kansai Network for Molecular Diagnosis of Central Nervous Tumors (KANSAI) were used as an exploratory cohort. From the Cancer Imaging Archive/Cancer Genome Atlas (TCGA) 93 patients were used as validation cohorts. "Thickened structure" was defined as the solid tumor component presenting circumferential extension or occupying >50% of the tumor volume. "Methylated contrast phenotype" was defined as indistinct enhancing circumferential border, heterogenous enhancement, or nodular enhancement. Inter-rater agreement was assessed, followed by an investigation of the relationship between radiological findings and pMGMT methylation status. Results: Fleiss's Kappa coefficient for "Thickened structure" was 0.68 for the exploratory and 0.55 for the validation cohort, and for "Methylated contrast phenotype," 0.30 and 0.39, respectively. The imaging feature, the presence of "Thickened structure" and absence of "Methylated contrast phenotype," was significantly predictive of pMGMT unmethylation both for the exploratory (p = .015, odds ratio = 2.44) and for the validation cohort (p = .006, odds ratio = 7.83). The sensitivities and specificities of the imaging feature, the presence of "Thickened structure," and the absence of "Methylated contrast phenotype" for predicting pMGMT unmethylation were 0.29 and 0.86 for the exploratory and 0.25 and 0.96 for the validation cohort. Conclusions: The present study showed that qualitative assessment of contrast-enhanced T1-weighted intensity images helps predict GBM's pMGMT methylation status.

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BACKGROUND: Glioblastoma is one of the deadliest tumors, and limited improvement in managing glioblastoma has been achieved in the past decades. The unmethylated promoter area of 6-O-Methylguanine-DNA Methyltransferase (MGMT) is a significant biomarker for recognizing a subset of glioblastoma that is resistant to chemotherapy. Here we identified MGMT methylation can also work as a specific biomarker to classify the lipid metabolism patterns between methylated and unmethylated glioblastoma and verify the potential novel therapeutic strategy for unmethylated MGMT glioblastoma. METHODS: Liquid Chromatograph Mass Spectrometer has been applied for non-targeted metabolome and targeted lipidomic profiling to explore the metabolism pattern correlated with MGMT promoter methylation. Transcriptome has been performed to explore the biological differences and the potential mechanism of lipid metabolism in glioblastoma samples. In vivo and ex vivo assays were performed to verify the anti-tumor activity of atorvastatin in the administration of glioblastoma. RESULTS: Multi-omics assay has described a significant difference in lipid metabolism between MGMT methylated and unmethylated glioblastoma. Longer and unsaturated fatty acyls were found enriched in MGMT-UM tumors. Lipid droplets have been revealed remarkably decreased in MGMT unmethylated glioblastoma. In vivo and ex vivo assays revealed that atorvastatin and also together with temozolomide showed significant anti-tumor activity, and atorvastatin alone was able to achieve better survival and living conditions for tumor-hosting mice. CONCLUSIONS: MGMT promoter methylation status might be a well-performed biomarker of lipid metabolism in glioblastoma. The current study can be the basis of further mechanism studies and implementation of clinical trials, and the results provide preclinical evidence of atorvastatin administration in glioblastoma, especially for MGMT unmethylated tumors.

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Glioblastoma (GBM) remains a treatment-resistant malignant brain tumor in large part because of its genetic heterogeneity and epigenetic plasticity. In this study, we investigated the epigenetic heterogeneity of GBM by evaluating the methylation status of the O6 -methylguanine methyltransferase (MGMT) promoter in individual clones of a single cell derived from GBM cell lines. The U251 and U373 GBM cell lines, from the Brain Tumour Research Centre of the Montreal Neurological Institute, were used for the experiments. To evaluate the methylation status of the MGMT promoter, pyrosequencing and methylation-specific PCR (MSP) were used. Moreover, mRNA and protein expression levels of MGMT in the individual GBM clones were evaluated. The HeLa cell line, which hyper-expresses MGMT, was used as control. A total of 12 U251 and 12 U373 clones were isolated. The methylation status of 83 of 97 CpG sites in the MGMT promoter were evaluated by pyrosequencing, and 11 methylated CpG sites and 13 unmethylated CpG sites were evaluated by MSP. The methylation status by pyrosequencing was relatively high at CpG sites 3-8, 20-35, and 7-83, in both the U251 and U373 clones. Neither MGMT mRNA nor protein was detected in any clone. These findings demonstrate tumor heterogeneity among individual clones derived from a single GBM cell. MGMT expression may be regulated, not only by methylation of the MGMT promoter but by other factors as well. Further studies are needed to clarify the mechanisms underlying the epigenetic heterogeneity and plasticity of GBM.

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OBJECTIVES: This study aims to explore the relationship between the methylation levels of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the structural connectivity in insular gliomas across hemispheres. METHODS: We analyzed 32 left and 29 right insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level. RESULTS: The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.019) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides. CONCLUSION: Our findings highlight a significant, hemisphere-specific correlation between MGMT promoter methylation and structural connectivity in insular gliomas. This study provides new insights into the genetic influence on glioma pathology, which could inform targeted therapeutic strategies.

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AIM OF THE STUDY: A molecular signature based on 10 mRNA abundances that characterizes the mesenchymal-to-proneural phenotype of glioblastoma stem(like) cells (GSCs) enriched in primary culture has been previously established. As this phenotype has been proposed to be prognostic for disease outcome the present study aims to identify features of the preoperative MR imaging that may predict the GSC phenotype of individual tumors. MATERIAL/METHODS: Molecular mesenchymal-to-proneural mRNA signatures and intrinsic radioresistance (SF4, survival fraction at 4 Gy) of primary GSC-enriched cultures were associated with survival data and pre-operative MR imaging of the corresponding glioblastoma patients of a prospective cohort (n = 24). The analyzed imaging parameters comprised linear vectors derived from tumor volume, necrotic volume and edema as contoured manually. RESULTS: A necrosis/tumor vector ratio and to a weaker extent the product of this ratio and the edema vector were identified to correlate with the mesenchymal-to-proneural mRNA signature and the SF4 of the patient-derived GSC cultures. Importantly, both parameter combinations were predictive for overall survival of the whole patient cohort. Moreover, the combination of necrosis/tumor vector ratio and edema vector differed significantly between uni- and multifocally recurring tumors. CONCLUSION: Features of the preoperative MR images may reflect the molecular signature of the GSC population and might be used in the future as a prognostic factor and for treatment stratification especially in the MGMT promotor-unmethylated sub-cohort of glioblastoma patients.

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OBJECTIVE: Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes. METHODS: Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. RESULTS: A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine. CONCLUSIONS: Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

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Glioblastoma is the most common malignant brain tumor in adults. Standard treatment includes tumor resection, radio-chemotherapy and adjuvant chemotherapy with temozolomide (TMZ). TMZ methylates DNA, whereas O6-methylguanine DNA methyltransferase (MGMT) counteracts TMZ effects by removing the intended proteasomal degradation signal. Non-functional MGMT mediates the mismatch repair (MMR) system, leading to apoptosis after futile repair attempts. This study investigated the associations between MGMT promoter methylation, MGMT and MMR protein expression, and their effect on overall survival (OS) and progression-free survival (PFS) in patients with glioblastoma. MGMT promoter methylation was assessed in 42 treatment-naïve patients with glioblastoma WHO grade IV by pyrosequencing. MGMT and MMR protein expression was analyzed using immunohistochemistry. MGMT promoter methylation was present in 52%, whereas patients <70 years of age revealed a significantly longer OS using a log-rank test and a significance threshold of p ≤ 0.05. MGMT protein expression and methylation status showed no correlation. MMR protein expression was present in all patients independent of MGMT status and did not influence OS and PFS. Overall, MGMT promoter methylation implicates an improved OS in patients with glioblastoma aged <70 years. In the elderly, the extent of surgery has an impact on OS rather than the MGMT promoter methylation or protein expression.

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BACKGROUND: As the largest concentration of neural stem cells in adult brain, the subventricular zone (SVZ) is considered to be a potential source of glioblastoma (GBM) occurrence in recent years. METHODS: In this study, 116 patients with glioblastoma treated at PLA General Hospital were retrospectively reviewed. The features of SVZ contacting glioblastoma were analyzed in terms of MR imaging and MGMT promoter methylation. We also evaluated the prognostic value of SVZ contacting in GBM patients. RESULTS: GBM with SVZ involvement on MRI is more likely to grow across the midline (36.8% vs. 6.9%, P=0.002), more often multifocal lesion (35.6% vs. 6.9%, P=0.003) and have a lower proportion of MGMT promoter methylation (36.8% vs. 69.0%, P=0.003). The median overall survival and progression- free survival of patients in the SVZ contacting group were 12 months and 7 months, while 25 months and 17 months in the non-contacting group (P<0.001, respectively). There was no significant difference in overall survival (P=0.229) and progression-free survival (P=0.808) between patients with different SVZ contacting regions. Multivariate survival analysis indicated that patients with MRI SVZ involvement showed worse overall survival (HR=2.060, 95%CI 1.195-3.550，P=0.009) and progression- free survival (HR=3.021, 95%CI 1.788-5.104，P＜0.001). CONCLUSION: This study suggested that MRI SVZ involvement at diagnosis is an independent risk factor for overall survival and progression-free survival in IDH wild-type glioblastoma patients. Based on MR imaging, we also found that SVZ contacting glioblastomas had a larger proportion of crossing midline tumors and multifocal lesions. In addition, patients with SVZ contact in our research presented a lower proportion of MGMT promoter methylation.

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Background: A newly developed brain molecular marker (BMM) data item was implemented by US cancer registries for individuals diagnosed with brain tumors in 2018-including IDH and 1p/19q-co-deletion statuses for adult-type diffuse gliomas. We thus investigated the testing/reporting completeness of BMM in the United States. Methods: Cases of histopathologically confirmed glioblastoma, astrocytoma, and oligodendroglioma diagnosed in 2018 were identified in the National Cancer Database. Adjusted odds ratios (ORadj) and 95% confidence intervals (CI) of BMM testing/reporting were evaluated for association with the selected patient, treatment, and facility-level characteristics using multivariable logistic regression. As a secondary analysis, predictors of MGMT promoter methylation testing/reporting among IDH-wildtype glioblastoma individuals were assessed. Key limitations of the BMM data item were that it did not include any details regarding testing technique or assay type and could not distinguish between a lack of testing and a lack of cancer registry reporting of testing results. Results: Among 8306 histopathologically diagnosed adult-type diffuse gliomas nationally, overall BMM testing/reporting completeness was 81.1%. Compared to biopsy-only cases, odds of testing/reporting increased for subtotal (ORadj= 1.38 [95% CI: 1.20-1.59], P < .001) and gross total resection (ORadj=1.50 [95% CI: 1.31-1.72], P < .001). Furthermore, the odds were lowest at community centers (hospitals (67.3%; ORadj=0.35 [95% CI: 0.26-0.46], P < .001) and highest at academic/NCI-designated comprehensive cancer centers (85.4%; referent). By geographical location, BMM testing/reporting completeness ranged from a high of 86.8% at New England (referent) to a low of 76.0 % in the West South Central region (ORadj=0.57 [95% CI: 0.42-0.78]; P < .001). Extent of resection, Commission-on-Cancer facility type, and facility location were additionally significant predictors of MGMT testing/reporting among IDH-wildtype glioblastoma cases. Conclusions: Initial BMM testing/reporting completeness for individuals with adult-type diffuse gliomas in the United States was promising, although patterns varied by hospital attributes and extent of resection.

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BACKGROUND: Molecularly-defined diffuse glioma types-including IDH-wildtype glioblastoma, IDH-mutant astrocytoma, IDH-mutant 1p/19q-codeleted oligodendroglioma, and H3 K27M-mutant diffuse midline glioma-were incorporated into U.S. cancer registry reporting for individuals with brain tumors beginning in 2018. We leveraged these new data to estimate the national-level overall survival (OS) patterns associated with glioma integrated diagnoses. METHODS: Individuals diagnosed with diffuse gliomas in 2018 and had brain molecular marker data were identified within the U.S. National Cancer Database. OS was estimated using Kaplan-Meier methods and stratified by WHO CNS grade, age, sex, tumor size, treatment, extent of resection, and MGMT promoter methylation. Additionally, the effects of WHO CNS grade were examined among individuals with IDH-wildtype astrocytic gliomas. RESULTS: 8651 individuals were identified. One-year OS was 53.7% for WHO grade 4 IDH-wildtype glioblastomas; 98.0%, 92.4%, and 76.3% for WHO grade 2, 3, and 4 IDH-mutant astrocytomas, respectively; 97.9% and 94.4% for WHO grade 2 and 3 IDH-mutant 1p/19q-codeleted oligodendrogliomas, respectively; and 55.9% for H3 K27M-mutant diffuse midline gliomas. Among IDH-wildtype glioblastomas, median OS was 17.1 months and 12.4 months for methylated and unmethylated MGMT promoters. Additionally, IDH-wildtype diffuse astrocytic gliomas reported as WHO grade 2 or 3 demonstrated longer OS compared to grade 4 tumors (both P < .001). CONCLUSIONS: Our findings provide the initial national OS estimates for molecularly-defined diffuse gliomas in the United States and illustrate the importance of incorporating such data into cancer registry reporting.

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The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

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Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

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Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.