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Medicine Feb 2024Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained...
BACKGROUND
Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patients, providing novel insights into diagnosis and more personalized treatment options. Deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients, more accurate treatment plan proposals, and accurate biomarker (IDH) prediction for gliomas. This systematic study examines the development of deep learning techniques for IDH prediction using histopathology images, spanning the period from 2019 to 2023.
METHOD
The study adhered to the PRISMA reporting requirements, and databases including PubMed, Google Scholar, Google Search, and preprint repositories (such as arXiv) were systematically queried for pertinent literature spanning the period from 2019 to the 30th of 2023. Search phrases related to deep learning, digital pathology, glioma, and IDH were collaboratively utilized.
RESULTS
Fifteen papers meeting the inclusion criteria were included in the analysis. These criteria specifically encompassed studies utilizing deep learning for the analysis of hematoxylin and eosin images to determine the IDH status in patients with gliomas.
CONCLUSIONS
When predicting the status of IDH, the classifier built on digital pathological images demonstrates exceptional performance. The study's predictive effectiveness is enhanced with the utilization of the appropriate deep learning model. However, external verification is necessary to showcase their resilience and universality. Larger sample sizes and multicenter samples are necessary for more comprehensive research to evaluate performance and confirm clinical advantages.
Topics: Humans; Brain Neoplasms; Deep Learning; Glioma; Biomarkers; Isocitrate Dehydrogenase; Mutation; Magnetic Resonance Imaging; Multicenter Studies as Topic
PubMed: 38363910
DOI: 10.1097/MD.0000000000037150 -
Journal of Cancer Research and Clinical... Jan 2024Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive... (Review)
Review
BACKGROUND
Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions.
METHODS
Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups.
RESULTS
By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category.
CONCLUSION
Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.
Topics: Humans; Glioblastoma; Artificial Intelligence; Reproducibility of Results; DNA Methylation; Brain Neoplasms; DNA Modification Methylases; DNA Repair Enzymes; DNA; Tumor Suppressor Proteins
PubMed: 38291266
DOI: 10.1007/s00432-023-05566-5 -
Neurosurgery Aug 2023Awake craniotomy (AC) is a common neurosurgical procedure for the resection of lesions in eloquent brain areas, which has the advantage of avoiding general anesthesia to...
BACKGROUND
Awake craniotomy (AC) is a common neurosurgical procedure for the resection of lesions in eloquent brain areas, which has the advantage of avoiding general anesthesia to reduce associated complications and costs. A significant resource limitation in low- and middle-income countries constrains the usage of AC.
OBJECTIVE
To review the published literature on AC in African countries, identify challenges, and propose pragmatic solutions by practicing neurosurgeons in Africa.
METHODS
We conducted a scoping review under Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Review guidelines across 3 databases (PubMed, Scopus, and Web of Science). English articles investigating AC in Africa were included.
RESULTS
Nineteen studies consisting of 396 patients were included. Egypt was the most represented country with 8 studies (42.1%), followed by Nigeria with 6 records (31.6%). Glioma was the most common lesion type, corresponding to 120 of 396 patients (30.3%), followed by epilepsy in 71 patients (17.9%). Awake-awake-awake was the most common protocol used in 7 studies (36.8%). Sixteen studies (84.2%) contained adult patients. The youngest reported AC patient was 11 years old, whereas the oldest one was 92. Nine studies (47.4%) reported infrastructure limitations for performing AC, including the lack of funding, intraoperative monitoring equipment, imaging, medications, and limited human resources.
CONCLUSION
Despite many constraints, AC is being safely performed in low-resource settings. International collaborations among centers are a move forward, but adequate resources and management are essential to make AC an accessible procedure in many more African neurosurgical centers.
Topics: Adult; Child; Humans; Africa; Brain Neoplasms; Craniotomy; Glioma; Wakefulness; Aged, 80 and over
PubMed: 36961213
DOI: 10.1227/neu.0000000000002453 -
Chinese Clinical Oncology Apr 2024The role of adjuvant radiotherapy (RT) after gross total resection (GTR) of the World Health Organization (WHO) grade II ependymoma is controversial. Therefore, we aimed... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The role of adjuvant radiotherapy (RT) after gross total resection (GTR) of the World Health Organization (WHO) grade II ependymoma is controversial. Therefore, we aimed to compare the outcomes of adjuvant RT against observation after GTR of WHO grade II ependymoma. We also compared the outcomes of adjuvant RT against observation after subtotal resection (STR) of WHO grade II ependymoma and performed further subgroup analysis by age and tumor location.
METHODS
PubMed and Embase were systematically reviewed for studies published up till 25 November 2022. Studies that reported individual-participant data on patients who underwent surgery followed by adjuvant RT/observation for WHO grade II ependymoma were included. The exposure was whether adjuvant RT was administered, and the outcomes were recurrence and overall survival (OS). Subgroup analyses were performed by the extent of resection (GTR or STR), tumor location (supratentorial or infratentorial), and age at the first surgery (<18 or ≥18 years old).
RESULTS
Of the 4,647 studies screened, three studies reporting a total of 37 patients were included in the analysis. Of these 37 patients, 67.6% (25 patients) underwent GTR, and 51.4% (19 patients) underwent adjuvant RT. Adjuvant RT after GTR was not significantly associated with both recurrence (odds ratio =5.50; 95% confidence interval: 0.64-60.80; P=0.12) and OS (P=0.16). Adjuvant RT was also not significantly associated with both recurrence and OS when the cohort was analyzed as a whole and on subgroup analysis by age and tumor location. However, adjuvant RT was associated with significantly longer OS after STR (P=0.03) with the median OS being 6.33 years, as compared to 0.40 years for patients who underwent STR followed by observation.
CONCLUSIONS
Based on our meta-analysis of 37 patients, administration of adjuvant RT after GTR was not significantly associated with improvement in OS or recurrence in patients with WHO grade II ependymoma. However, due to the small number of patients included in the analysis, further prospective controlled studies are warranted.
Topics: Humans; Ependymoma; Radiotherapy, Adjuvant; Female; Male; Neoplasm Grading; World Health Organization
PubMed: 38644544
DOI: 10.21037/cco-23-136 -
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