ArticleJournal of neuro-oncology2022
Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients.
Article 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 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.Journal of neuro-oncology · 2026Pooled it
- Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Radiomics and comparative neuroimaging of canine gliomas and human glioblastomas: a step toward precision medicine.Frontiers in veterinary science · 2026Article
- Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.Scientific reports · 2025Article
- Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI.Diagnostics (Basel, Switzerland) · 2025Review
- Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma.PloS one · 2025Article
- Correlations ofMolecular and clinical oncology · 2025Article
- Article
- Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma.Cancers · 2024Article
- Artificial intelligence innovations in neurosurgical oncology: a narrative review.Journal of neuro-oncology · 2024Review
- Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas.Clinical neuroradiology · 2024Review
- Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study.Neuroradiology · 2024Article
- Development of A Radiomic Model forInternational journal of molecular sciences · 2023Article
- Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.Journal of neuro-oncology · 2023Article
- Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.Journal of magnetic resonance imaging : JMRI · 2023Review
- An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.Frontiers in oncology · 2023Article
- Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAlthough glioblastoma (GBM) is the most common primary brain malignancy, few tools exist to pre-operatively risk-stratify patients by overall survival (OS) or common genetic alterations. We developed an MRI-based radiomics model to identify patients with EGFR amplification, MGMT methylation, GBM subtype, and OS greater than 12 months.
methodsWe retrospectively identified 235 patients with pathologically confirmed GBMs from the Cancer Genome Atlas (88; TCGA) and MD Anderson Cancer Center (147; MDACC). After two neuroradiologists segmented MRI tumor volumes, we extracted first-order and second-order radiomic features (gray-level co-occurrence matrices). We used the Maximum Relevance Minimum Redundancy technique to identify the 100 most relevant features and validated models using leave-one-out-cross-validation and validation on external datasets (i.e., TCGA). Our results were reported as the area under the curve (AUC).
resultsThe MDACC patient cohort had significantly higher OS (22 months) than the TCGA dataset (14 months). On both LOOCV and external validation, our radiomics models were able to identify EGFR amplification (all AUCs > 0.83), MGMT methylation (all AUCs > 0.85), GBM subtype (all AUCs > 0.92), and OS (AUC > 0.91 on LOOCV and 0.71 for TCGA validation).
conclusionsOur robust radiomics pipeline has the potential to pre-operatively discriminate common genetic alterations and identify patients with favorable survival.
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