ArticleJournal of personalized medicine2021
Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- 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
- A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.Radiologie (Heidelberg, Germany) · 2026Article
- Radiomics-Driven Hybrid Deep Learning for MRI-Based Prediction of Glioma Grade and 1p/19q Codeletion.Tomography (Ann Arbor, Mich.) · 2026Article
- Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.BMC cancer · 2025Article
- A Review of Artificial Intelligence-Based Systems for Non-Invasive Glioblastoma Diagnosis.Life (Basel, Switzerland) · 2025Review
- Canadian Expert Consensus Recommendations for the Diagnosis and Management of Glioblastoma: Results of a Delphi Study.Current oncology (Toronto, Ont.) · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Review
- Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning.Science advances · 2024Article
- Expression and prognostic significance of microsomal triglyceride transfer protein in brain tumors: a retrospective cohort study.Translational cancer research · 2024Article
- Advanced magnetic resonance imaging for glioblastoma: Oncology-radiology integration.Surgical neurology international · 2024Article
- Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.Diagnostics (Basel, Switzerland) · 2023Article
- MRI-Based Deep Learning Method for Classification of IDH Mutation Status.Bioengineering (Basel, Switzerland) · 2023Article
- Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging.Life (Basel, Switzerland) · 2023Review
- 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.Journal of digital imaging · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.
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Registered trials
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