SynthesisThe British journal of radiology2018
Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review.
Synthesis in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis.Cancer medicine · 2019Pooled it
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- Deep learning-based overall survival prediction in patients with glioblastoma: An automatic end-to-end workflow using pre-resection basic structural multiparametric MRIs.Computers in biology and medicine · 2025Article
- Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas.Clinical neuroradiology · 2024Review
- Post-chemo-radiotherapy response and pseudo-progression evaluation on glioma cell types by multi-parametric magnetic resonance imaging: a prospective study.BMC medical imaging · 2023Article
- Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.Journal of neuro-oncology · 2023Article
- An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.Journal of translational medicine · 2023Article
- Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.Frontiers in molecular neuroscience · 2023Article
- MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation.Frontiers in medicine · 2023Article
- Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs.Physics in medicine and biology · 2022Article
- Review
- Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.Cancers · 2022Review
- Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.Frontiers in neurology · 2022Review
- Artificial Intelligence and Precision Medicine: A Perspective.Advances in experimental medicine and biology · 2022Article
- Radiomic Features Associated with Extent of Resection in Glioma Surgery.Acta neurochirurgica. Supplement · 2022Review
- Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.Frontiers in neurology · 2022Article
- Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging.Neuroradiology · 2021Article
- Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging.Insights into imaging · 2021Review
- Article
- Sialic Acid-Modified Nanoparticles-New Approaches in the Glioma Management-Perspective Review.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objective: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features.
methods: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging.
results: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented.
conclusion: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE:: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
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