ArticleScientific reports2020
A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Pooled it
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Artificial Intelligence for the Preoperative Molecular Characterization of Adult-Type Diffuse High-Grade Gliomas: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Review
- MRI radiomic signature predicts peritumoral brain edema resolution following meningioma surgery.Acta neurochirurgica · 2025Article
- Predicting glioma histo-molecular diagnosis and prognosis: preoperative dynamic contrast-enhanced magnetic resonance imaging insights.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.BMC cancer · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.Medical physics · 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
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Comparison of intensity normalization methods in prostate, brain, and breast cancer multi-parametric magnetic resonance imaging.Frontiers in oncology · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma.Bioengineering (Basel, Switzerland) · 2024Article
- Challenges in LncRNA Biology: Views and Opinions.Non-coding RNA · 2024Article
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).
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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.