ReviewFrontiers in oncology2021
Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures.
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
14 citing papers in PubMed.
- Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.Journal of translational medicine · 2026Article
- Beyond morphology: imaging the glioblastoma microenvironment in the era of quantitative neuro-oncology.Frontiers in medicine · 2026Review
- 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
- Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.Scientific reports · 2025Article
- Survival and immune microenvironment prediction of glioma based on MRI imaging genomics method: a retrospective observational study.Neurosurgical review · 2025Observational
- Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation.Cancer management and research · 2025Review
- Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.Current oncology reports · 2024Review
- Breaking Barriers: A Future Perspective on Glioblastoma Therapy with mRNA-Based Immunotherapies and Oncolytic Viruses.Vaccines · 2024Article
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques.Biomedicines · 2023Review
- Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis.ACS omega · 2023Review
- Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.The British journal of radiology · 2022Article
- Surgical approaches to intramedullary spinal cord astrocytomas in the age of genomics.Frontiers in oncology · 2022Review
- Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.
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Registered trials
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