ReviewJournal of personalized medicine2022
Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review.
Review in Journal of personalized medicine, 2022. 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.
- Precision reirradiation in recurrent CNS tumors integrating biology techniques and multimodal care.Discover oncology · 2026Review
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Biomarkers for predicting immunotherapy response and resistance in glioblastoma.Frontiers in immunology · 2026Review
- Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.Journal of cancer research and clinical oncology · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
- 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
- Multimodal MRI andDiagnostics (Basel, Switzerland) · 2024Article
- Article
- Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2024Review
- Concept of a fully-implantable system to monitor tumor recurrence.Scientific reports · 2023Article
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- XGBoost algorithm and logistic regression to predict the postoperative 5-year outcome in patients with glioma.Annals of translational medicine · 2022Article
- MRI-defined patterns of infiltration and outcome in patients with glioblastoma.Neuro-oncology advancesArticle
- 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
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