ArticleNeuro-oncology advances
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 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
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.Insights into imaging · 2026Article
- The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.Scientific data · 2026Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas.NPJ precision oncology · 2025Article
- DEP domain‑containing 1 is a prognostic biomarker associated with the cell cycle in gliomas.Oncology letters · 2025Article
- Detection of IDH mutation in glioma by desorption electrospray ionization (DESI) tandem mass spectrometry.Scientific reports · 2024Article
- IDH inhibition in gliomas: from preclinical models to clinical trials.Nature reviews. Neurology · 2024Review
- Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Elucidating the impact of parthanatos-related microRNAs on the tumoral immune microenvironment and clinical outcome in low-grade gliomas.Discover oncology · 2024Article
- Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review.Cancers · 2024Article
- Machine Learning-Based Prediction of GliomaCancers · 2024Article
- Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).JCO clinical cancer informatics · 2023Article
- Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Unsupervised learning of metabolic fingerprints from 3D magnetic resonance spectroscopic imaging enables glioma subtype classification.Neuro-oncology advancesArticle
- Recent Advancements and Future Perspectives on Molecular Biomarkers in Adult Lower-Grade Gliomas.Cancer journal (Sudbury, Mass.)Review
- Bridging the clinical gap: Confidence informed IDH prediction in brain gliomas using MRI and deep learning.Neuro-oncology advancesArticle
- Interpretable artificial intelligence based determination of glioma IDH mutation status directly from histology slides.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Background: IDH mutation and 1p/19q codeletion status are important prognostic markers for glioma that are currently determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to noninvasively determine molecular alterations from MRI. Methods: Pre-operative MRI scans of 2648 glioma patients were collected from Washington University School of Medicine (WUSM; Results: For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. Conclusions: The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform "virtual biopsy" for tailoring treatment planning and overall clinical management of gliomas.
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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.