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International Journal of Surgical... Jun 2024Epithelioid glioblastoma (E-GBM) is an exceedingly rare subtype of isocitrate dehydrogenase (IDH)-wildtype glioblastoma, first included in the WHO 2016 classification... (Review)
Review
Epithelioid glioblastoma (E-GBM) is an exceedingly rare subtype of isocitrate dehydrogenase (IDH)-wildtype glioblastoma, first included in the WHO 2016 classification and characterized by a dominant population of epithelioid cells. Its histological and molecular defining features remain troublesome. The significance of mutations to pathological diagnosis and surgical outcome has drawn increasing attention given their promising potential for future adjuvant therapies. Herein, we describe a unique case of an E-GBM in the atrium of the left lateral ventricle and comprehensively analyze the importance of status in a cohort of 211 E-GBMs from the literature. Our patient was a 40-year-old man with occipital pain. His brain MRI revealed a large intraventricular tumor at the same location as a signal change found 10 years earlier with no additional follow-up. He underwent gross total tumor removal followed by conventional adjuvant treatment. Histopathological diagnosis was consistent with IDH-wildtype E-GBM WHO grade 4 with pleomorphic xanthoastrocytoma-like areas. p.V600 mutation was demonstrated in the tumoral genetic study. In the cohort analyzed, male patients predominated (63%), the median age was 32 years old, and the 5-year survival rate following diagnosis was 4.2%. mutations were found in 60.3% of the tumors overall, with this rate increasing to 78.3% in young adults (19-49 years, < .001). Presence of mutations associated with tumor progression ( = .001), the event usually leading to death ( < .001). In conclusion, our study supports the importance of genetic p.V600 mutation analysis because its presence not only points to an E-GBM diagnosis but may also promote tumor progression.
Topics: Adult; Female; Humans; Male; Biomarkers, Tumor; Brain Neoplasms; Cerebral Ventricle Neoplasms; Glioblastoma; Magnetic Resonance Imaging; Mutation; Proto-Oncogene Proteins B-raf; Middle Aged
PubMed: 37743598
DOI: 10.1177/10668969231195026 -
Current Reviews in Clinical and... 2024Recurrent glioblastoma multiforme (rGBM) has a grim prognosis, with current therapies offering no survival benefit. Several combination therapies involving anti-VEGF... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Recurrent glioblastoma multiforme (rGBM) has a grim prognosis, with current therapies offering no survival benefit. Several combination therapies involving anti-VEGF agents have been studied with mixed results.
METHODS
A systematic search was performed using five electronic databases: PubMed, Scopus, ISI, Embase, and the Cochrane Library, without language limitations. The primary outcome of interest was progression-free survival (PFS). Secondary outcomes were overall survival (OS), objective response ratio (ORR), and grade ≥ 3 adverse events. Estimates for PFS and OS were calculated as random effects hazard ratio (HR) with 95% confidence intervals (CIs) using the generic inverse variance method. Estimates for ORR and grade ≥ 3 adverse events were calculated using a random-effects risk ratio (RR) with 95% confidence intervals (CIs) using the Mantel-Haenszel method.
RESULTS
Thirteen studies met the inclusion criteria and a total of 1994 patients were included in the analysis. There was no statistically significant improvement in PFS (HR 0.84; 95% CI (0.68, 1.03); I=81%), OS (HR 0.99; 95% CI (0.88, 1.12); I=0%), and ORR (RR 1.36; 95% CI (0.96, 1.92); I=61%) in the combination therapy group when compared to the control group. Significantly higher grade ≥ 3 adverse events (RR 1.30; 95% CI (1.14, 1.48); I=47%) were seen in the combination therapy when compared to the control group.
CONCLUSION
Our analysis showed that the use of combination therapy with anti-VEGF agents did not offer any benefit in PFS, OS, or ORR. In contrast, it had significantly higher grade 3-5 adverse events. Further studies are needed to identify effective therapies in rGBM that can improve survival.
Topics: Humans; Glioblastoma; Neoplasm Recurrence, Local; Vascular Endothelial Growth Factor A
PubMed: 35585804
DOI: 10.2174/2772432817666220517163609 -
World Neurosurgery Mar 2024
Meta-Analysis
Topics: Humans; Glioblastoma; Brain Neoplasms; Neurosurgical Procedures
PubMed: 38468167
DOI: 10.1016/j.wneu.2023.12.019 -
BMC Cancer May 2024Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is...
BACKGROUND
Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.
METHODS
A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.
RESULTS
From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.
CONCLUSION
We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.
Topics: Glioblastoma; Humans; Computational Biology; Biomarkers, Tumor; Prognosis; Brain Neoplasms; RNA, Messenger; Protein Interaction Maps; Gene Expression Regulation, Neoplastic; Gene Expression Profiling
PubMed: 38773447
DOI: 10.1186/s12885-024-12345-z -
Clinical Radiology Jun 2024Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We... (Meta-Analysis)
Meta-Analysis Comparative Study
How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review.
BACKGROUND
Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We aimed to perform this meta-analysis to evaluate the diagnostic accuracy of ML/DL versus radiologists in classifying PCNSL versus GBM using MRI.
METHODOLOGY
The study was performed in accordance with PRISMA guidelines. Data was extracted and interpreted by two researchers with 12 and 23 years' experience, respectively, and QUADAS-2 tool was used for quality and risk-bias assessment. We constructed contingency tables to derive sensitivity, specificity accuracy, summary receiver operating characteristic (SROC) curve, and the area under the curve (AUC).
RESULTS
Our search identified 11 studies, of which 8 satisfied our inclusion criteria and restricted the analysis in each study to reporting the model showing highest accuracy, with a total sample size of 1159 patients. The random effects model showed a pooled sensitivity of 0.89 [95% CI:0.84-0.92] for ML and 0.82 [95% CI:0.76-0.87] for radiologists. Pooled specificity was 0.88 [95% CI: 0.84-0.91] for ML and 0.90 [95% CI: 0.81-0.95] for radiologists. Pooled accuracy was 0.88 [95% CI: 0.86-0.90] for ML and 0.86 [95% CI: 0.78-0.91] for radiologists. Pooled AUC of ML was 0.94 [95% CI:0.92-0.96]and for radiologists, it was 0.90 [95% CI: 0.84-0.93].
CONCLUSIONS
MRI-based ML/DL techniques can complement radiologists to improve the accuracy of classifying GBMs from PCNSL, possibly reduce the need for a biopsy, and avoid any unwanted neurosurgical resection of a PCNSL.
Topics: Humans; Deep Learning; Diagnosis, Differential; Machine Learning; Glioblastoma; Lymphoma; Magnetic Resonance Imaging; Brain Neoplasms; Sensitivity and Specificity; Radiologists; Central Nervous System Neoplasms; Astrocytoma
PubMed: 38614870
DOI: 10.1016/j.crad.2024.03.007