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Journal of Clinical Medicine Jun 2024Pilocytic astrocytoma (PCA) are commonly observed as slow-growing noncancerous brain tumors in pediatric populations, but they can also occur in adults, albeit rarely.... (Review)
Review
Pilocytic astrocytoma (PCA) are commonly observed as slow-growing noncancerous brain tumors in pediatric populations, but they can also occur in adults, albeit rarely. When located in diencephalic regions, particularly in the hypothalamus, they present unique diagnostic and management challenges due to their rarity and overlapping clinical and radiological features with other intracranial pathologies. This systematic review aims to provide a comprehensive understanding of hypothalamic PCA in adults, focusing on their differential diagnosis, neurological presentation, diagnostic modalities, treatment strategies. A case illustration is also described in order to better underline all the difficulties related to the diagnostic process. A systematic literature search was conducted in the PubMed/MEDLINE, Embase, and Scopus databases up to November 2023 to identify studies. The systematic literature search identified a total of 214 articles. Following screening by title and abstract and full-text review, 12 studies were deemed eligible and are included here. Adult-onset PCA in diencephalic regions pose diagnostic challenges due to their rarity and overlapping features with other intracranial lesions. Advanced imaging techniques play a crucial role in diagnosis, while surgery remains the cornerstone of treatment. Multidisciplinary collaboration is essential for the optimal management and long-term follow-up of these patients.
PubMed: 38930064
DOI: 10.3390/jcm13123536 -
Revista Da Associacao Medica Brasileira... 2024
Review
Topics: Humans; Glioblastoma; Palliative Care; Brain Neoplasms
PubMed: 38865542
DOI: 10.1590/1806-9282.2024S122 -
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 -
Critical Reviews in Oncology/hematology Jun 2024This systematic review summarizes evidence of VEGFR gene mutations and VEGF/VEGFR protein expression in glioblastoma multiforme (GBM) patients, alongside the efficacy... (Review)
Review
PURPOSE
This systematic review summarizes evidence of VEGFR gene mutations and VEGF/VEGFR protein expression in glioblastoma multiforme (GBM) patients, alongside the efficacy and safety of anti-VEGFR tyrosine kinase inhibitors (TKIs) for GBM treatment.
METHODS
A comprehensive literature review was conducted using PubMed up to August 2023. Boolean operators and MeSH term "glioma," along with specific VEGFR-related keywords, were utilized following thorough examination of existing literature.
RESULTS
VEGFR correlates with glioma grade and GBM progression, presenting a viable therapeutic target. Regorafenib and axitinib show promise among studied TKIs. Other multi-targeted TKIs (MTKI) and combination therapies exhibit potential, albeit limited by blood-brain barrier penetration and toxicity. Combining treatments like radiotherapy and enhancing BBB penetration may benefit patients. Further research is warranted in patient quality of life and biomarker-guided selection.
CONCLUSION
While certain therapies hold promise for GBM, future research should prioritize personalized medicine and innovative strategies for improved treatment outcomes.
Topics: Humans; Glioblastoma; Protein Kinase Inhibitors; Receptors, Vascular Endothelial Growth Factor; Brain Neoplasms; Antineoplastic Agents
PubMed: 38677355
DOI: 10.1016/j.critrevonc.2024.104365 -
Neurosurgical Review Apr 2024We aim to investigate the efficacy and safety of laser interstitial thermal therapy (LITT) in treating recurrent glioblastomas (rGBMs). A comprehensive search was... (Meta-Analysis)
Meta-Analysis Review
We aim to investigate the efficacy and safety of laser interstitial thermal therapy (LITT) in treating recurrent glioblastomas (rGBMs). A comprehensive search was conducted in four databases to identify studies published between January 2001 and June 2022 that reported prognosis information of rGBM patients treated with LITT as the primary therapy. The primary outcomes of interest were progression-free survival (PFS) and overall survival (OS) at 6 and 12 months after LITT intervention. Adverse events and complications were also evaluated. Eight eligible non-comparative studies comprising 128 patients were included in the analysis. Seven studies involving 120 patients provided data for the analysis of PFS. The pooled PFS rate at 6 months after LITT was 25% (95% CI 15-37%, I = 53%), and at 12 months, it was 9% (95% CI 4-15%, I = 24%). OS analysis was performed on 54 patients from six studies, with an OS rate of 92% (95% CI 84-100%, I = 0%) at 6 months and 42% (95% CI 13-73%, I = 67%) at 12 months after LITT. LITT demonstrates a favorable safety profile with low complication rates and promising tumor control and overall survival rates in patients with rGBMs. Tumor volume and performance status are important factors that may influence the effectiveness of LITT in selected patients. Additionally, the combination of LITT with immune-based therapy holds promise. Further well-designed clinical trials are needed to expand the application of LITT in glioma treatment.
Topics: Humans; Glioblastoma; Glioma; Databases, Factual; Progression-Free Survival; Lasers
PubMed: 38625588
DOI: 10.1007/s10143-024-02409-w -
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 -
World Neurosurgery Jun 2024Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types.
METHODS
A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity.
RESULTS
Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00).
CONCLUSIONS
ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.
Topics: Humans; Brain Neoplasms; Glioblastoma; Glioma; Machine Learning; Sensitivity and Specificity
PubMed: 38580093
DOI: 10.1016/j.wneu.2024.03.152 -
World Neurosurgery Jun 2024Pilocytic astrocytomas (PA) are the most common gliomas in children/adolescents but are less common and poorly studied in adults. Here, we describe the clinical...
BACKGROUND
Pilocytic astrocytomas (PA) are the most common gliomas in children/adolescents but are less common and poorly studied in adults. Here, we describe the clinical presentation, surgical management, and outcomes of surgically treated adult patients with intraventricular (IV) PA and review the literature.
METHODS
Consecutive adult patients treated for IV brain tumors at a tertiary academic center over 25 years (1997-2023) were identified. Clinical data were reviewed retrospectively for adult IV PA patients. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.
RESULTS
Eight patients with IV PA were included. Median age was 25 years (range, 18-69 years), and 4 (50%) were female. The most common tumor location was the lateral ventricle (5, 63%), followed by the fourth ventricle (3, 37%). Subtotal and near total resection were the most common surgical outcomes (6 patients, 75%), followed by gross total resection in 2 (25%). Progression or recurrence occurred in 3 patients (37%), requiring repeat resection in 2 patients. The 5-year overall survival and progression-free survival were 67% and 40%, respectively. In addition, 42 cases were identified in the literature.
CONCLUSIONS
PAs in adults are rare and an IV location is even more uncommon. The findings demonstrate the challenges in caring for these patients, with overall- and progression-free survival outcomes being poorer than the general adult PA population. Findings support the employment of surgical techniques and approaches that favor gross total resection when possible. Further studies are needed to better characterize this unique presentation.
Topics: Humans; Astrocytoma; Adult; Female; Young Adult; Cerebral Ventricle Neoplasms; Middle Aged; Adolescent; Male; Aged; Neurosurgical Procedures; Retrospective Studies; Neoplasm Recurrence, Local; Treatment Outcome
PubMed: 38580091
DOI: 10.1016/j.wneu.2024.03.153 -
Journal of Neurosurgery. Pediatrics Apr 2024More than one-third of pediatric patients who undergo resection of intradural spine lesions develop progressive postoperative deformity, with as many as half of these...
OBJECTIVE
More than one-third of pediatric patients who undergo resection of intradural spine lesions develop progressive postoperative deformity, with as many as half of these patients subsequently requiring surgical fusion. Intradural spinal procedures with simultaneous instrumented fusion in children, however, are infrequently performed. Moreover, the rationale for patient selection, outcomes, and safety of this single-stage surgery in children has not been systematically investigated. In this study, the authors review the practice of simultaneous intradural spinal resection and instrumented fusion in pediatric patients and provide two representative case examples from their institution.
METHODS
The authors searched the PubMed and Embase databases and performed a systematic review following the PRISMA protocol. Original articles of pediatric patients (age ≤ 18 years) who underwent intradural spine surgery, regardless of pathology, with concomitant instrumented fusion and reported outcomes were included. An institutional database of all spinal operations with instrumented fusion performed in patients aged ≤ 18 years over a 3-year period was screened to identify those who underwent intradural spine surgery with concomitant fusion.
RESULTS
Nine patients (median age 12 years) from 6 studies who underwent intradural lesion resection and concomitant fusion met inclusion criteria. Among all 11 patients included, primary rationales for concomitant fusion were extensive bone removal (i.e., corpectomy or total facetectomy, 73%), concerns for deformity in the setting of multilevel laminectomy/laminoplasty (18%), and severe baseline deformity (9%). The most represented pathology was neurenteric cyst (55%) followed by schwannoma (18%). Myxopapillary ependymoma, granular cell tumor, and pilocytic astrocytoma each were seen in 1 case. Seven patients (64%) underwent an anterior-approach corpectomy, tumor resection, and fusion, while the remaining 4 patients (36%) underwent a posterior approach. All patients with at least 1 year of follow-up cases achieved bony fusion. CSF leak and new-onset neurological deficit each occurred in 9% (1/11).
CONCLUSIONS
The rationales for performing single-stage intradural resection and fusion in pediatric patients in studies to date include the presence of severe baseline deformity, large extent of bone resection, and multilevel laminectomy/laminoplasty across cervicothoracic or thoracolumbar junctions. As current literature involving this cohort is limited, more data are needed to determine when concomitant fusion in intradural resections is appropriate in pediatric patients and whether its routine implementation is safe or beneficial.
PubMed: 38579345
DOI: 10.3171/2024.1.PEDS23444 -
Current Pharmaceutical Design 2024The prognosis for primary brain tumors, like other CNS tumors, can vary greatly based on several factors, such as treatment history, age and gender at diagnosis, ethnic... (Review)
Review
BACKGROUND
The prognosis for primary brain tumors, like other CNS tumors, can vary greatly based on several factors, such as treatment history, age and gender at diagnosis, ethnic background, and treatment plan.
MATERIALS AND METHODS
A systematic review approach was used to gather relevant data from PubMed, ScienceDirect, Google Scholar, and other sources.
RESULTS
The survival rate of primary brain tumors and other CNS tumors appears to be correlated with several variables, including treatment history, gender, age at evaluation, race/ethnicity, and treatment regimen; this emphasizes the importance of routinely updating epidemiological data on primary brain tumors to advance biological understanding.
CONCLUSION
This study draws attention to the variations in the median survival times of the various kinds of primary brain tumors, with oligodendroglioma having the longest median survival time (199 months, or approximately 16.6 years) and glioblastoma having the shortest (8 months).
Topics: Humans; Brain Neoplasms; Survival Rate; Prognosis; Data Analysis; Glioblastoma
PubMed: 38571355
DOI: 10.2174/0113816128306113240328050608