ArticleAJNR. American journal of neuroradiology2018
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.
Article in AJNR. American journal of neuroradiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 219 papers, 3 of them syntheses 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
219 citing papers in PubMed, 3 syntheses or guidelines pooled it, 479 citations in OpenAlex.
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- MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies.Neuroradiology · 2025Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.European radiology · 2022Trial
- Spatial and quantitative mapping of glycolysis and hypoxia in glioblastoma as a predictor of radiotherapy response and sites of relapse.European journal of nuclear medicine and molecular imaging · 2020Trial
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- Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI.Research square · 2026Article
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Improving MRI-based differentiation of brain metastasis, glioblastoma, and primary CNS lymphoma using deep learning.Neuroradiology · 2026Article
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- The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.Scientific data · 2026Article
- Brain foundation models with hypergraph dynamic adapter for brain disease analysis.Pattern recognition · 2026Article
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- Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction.JCO precision oncology · 2026Article
- A 3D feature fusion model integrating multi-scale MRI feature for interpretable Glioblastoma prediction.Medical & biological engineering & computing · 2026Article
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
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- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Personalized chronotherapy in glioblastoma: integrating circadian profiling and PK-PD modelling to optimize temozolomide timing.NPJ precision oncology · 2025Article
159 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors at 11 institutions in 2 countries.
Funding
Abstract
background and purposeThe World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MATERIALS AND
methodsMR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify
resultsClassification had high accuracy:
conclusionsOur results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.
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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.