ReviewJournal of neuro-oncology2022
Radiomics for precision medicine in glioblastoma.
Review in Journal of neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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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.
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Who cites it
37 citing papers in PubMed, 51 citations in OpenAlex.
- Artificial Intelligence-Based MRI Segmentation in Glioblastoma and Single Brain Metastasis: An Exploratory Study of Diagnostic and Prognostic Value.Life (Basel, Switzerland) · 2026Article
- Evolving Landscape of Glioblastoma Research: Integrating Therapeutic Advances and Diagnostic Frontiers.Brain sciences · 2026Review
- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
- Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction.Medical sciences (Basel, Switzerland) · 2026Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Non-invasive identification of mesenchymal glioblastoma using quantitative radiomic features from advanced diffusion MRI: a preclinical-to-clinical transfer learning strategy.European radiology experimental · 2025Article
- Review
- Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.BMC medical imaging · 2025Article
- From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Neurosurgical review · 2025Article
- Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities.Nature communications · 2025Article
- MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model.Biomedicines · 2025Article
- Predicting postoperative recurrence and survival in glioma patients using enhanced MRI-based delta habitat radiomics: an 8-year retrospective pilot study.World journal of surgical oncology · 2025Article
- Advances in Glioblastoma Diagnosis: Integrating Genetics, Noninvasive Sampling, and Advanced Imaging.Cancers · 2025Review
- Explainable, modality-adaptive radiomics for MGMT methylation prediction in high-grade glioma: a decision-curve analysis study.Frontiers in oncology · 2025Article
- Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms.Frontiers in oncology · 2025Review
- Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.Current oncology reports · 2024Review
- Cognitive functioning in untreated glioma patients: The limited predictive value of clinical variables.Neuro-oncology · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Article
- Progress in the application of ultrasound in glioma surgery.Frontiers in medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionBeing the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.
methodsWe performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.
resultsClassifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.
conclusionRadiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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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.