ReviewCancer reports (Hoboken, N.J.)2019
Machine learning approaches to study glioblastoma: A review of the last decade of applications.
Review in Cancer reports (Hoboken, N.J.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Glioblastoma multiforme (GBM): An overview of current therapies and mechanisms of resistance.Pharmacological research · 2021Pooled it
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.Acta neurochirurgica · 2025Article
- Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme.Discover oncology · 2025Article
- Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme.Brain communications · 2025Article
- Schistosomiasis transmission: A machine learning analysis reveals the importance of agrochemicals on snail abundance in Rwanda.PLoS neglected tropical diseases · 2024Article
- Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges.International journal of molecular sciences · 2024Review
- Article
- Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers.International journal of molecular sciences · 2023Review
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
- Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review.Metabolites · 2023Review
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
- Application of machine learning to predict the outcome of pediatric traumatic brain injury.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2021Article
- Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective.The protein journal · 2021Review
- Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models.The neuroradiology journal · 2021Article
- Deep learning applications in neuro-oncology.Surgical neurology international · 2021Article
- Machine learning approaches to study glioblastoma: A review of the last decade of applications.Cancer reports (Hoboken, N.J.) · 2019Review
- Uses of artificial intelligence in glioma: A systematic review.Medicine internationalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urgent to characterize the mechanisms of GB pathogenesis to help researchers identify novel therapeutic targets to cure this devastating disease. Recently, a promising approach to identifying novel therapeutic targets is the integration of tumor omics data with clinical information using machine learning (ML) techniques. RECENT
findingsML has become a valuable addition to the researcher's toolbox, thanks to its flexibility, multidimensional approach, and a growing community of users. The goal of this review is to introduce basic concepts and applications of ML for studying GB to clinicians and practitioners who are new to data science. ML applications include exploring large data sets, finding new relevant patterns, predicting outcomes, or merely understanding associations of the complex molecular networks presented within the tumor. Here, we review ML applications published between 2008 and 2018 and discuss ML strategies intending to identify new potential therapeutic targets to improve the management and treatment of GB.
conclusionsML applications to study GB vary in purpose and complexity, with positive results. In GB studies, ML is often used to analyze high-dimensional datasets with prediction or classification as a primary goal. Despite the strengths of ML techniques, they are not fail-safe and methodological issues can occur in GB studies that use them. This is why researchers need to be aware of these issues when planning and appraising studies that apply ML to the study of GB.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.