ArticleFrontiers in neurology2025
Magnetic resonance imaging-based deep learning for predicting subtypes of glioma.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Three-Dimensional Deep Learning with Routine Brain Magnetic Resonance Imaging and Clinical Data for Identification of Secondary Progressive Multiple Sclerosis.Brain sciences · 2026Article
- Customized SAM-Med3D with multi-view adapter and T2-FLAIR mismatch features for glioma IDH genotyping and grading.Frontiers in behavioral neuroscience · 2025Article
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6 authors.
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Abstract
Purpose: To explore the value of deep learning based on magnetic resonance imaging (MRI) in the classification of glioma subtypes. Methods: This study retrospectively included 747 adult patients with surgically pathologically confirmed gliomas from a public database and 64 patients from our hospital. Patients were classified into IDH-wildtype (IDHwt) (490 cases), IDH-mutant/1p19q-noncodeleted (IDHmut-intact) (105 cases), and IDH-mutant/1p19q-codeleted (IDHmut-codel) (216 cases) based on their pathological findings, with the public database of patients were divided into training and validation sets, and patients from our hospital were used as an independent test set. The models were developed based on five categories of preoperative T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted and T2-weighted fluid-attenuated inversion recovery (T1w, T1c, T2w and FLAIR) magnetic resonance imaging (MRI) of four sequences and mixed imaging of the four sequences, respectively. The receiver operating characteristic curve (ROC), area under the curve (AUC) of the ROC were generated in the jupyter notebook tool using python language to evaluate the accuracy of the models in classification and comparing the predictive value of different MRI sequences. Results: IDHwt, IDHmut-intact and IDHmut-codel were the best classified in the model containing only FLAIR sequences, with test set AUCs of 0.790, 0.737 and 0.820, respectively; and the worst classified in the model containing only T1w sequences, with test set AUCs of 0.621, 0.537 and 0.760, respectively. Conclusion: We have developed a set of models that can effectively classify glioma subtypes and that work best when only the FLAIR sequence model is included.
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