ArticleNeuro-oncology2019
Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.
Article in Neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 4 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 4 syntheses or guidelines 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
- Genetic Variants Impacting Angiogenesis Signaling Pathways in Glioblastoma Multiforme: A Systematic Review of Mutations and Polymorphisms.Current molecular medicine · 2024Pooled it
- The role of DSC MR perfusion in predicting IDH mutation and 1p19q codeletion status in gliomas: meta-analysis and technical considerations.Neuroradiology · 2023Pooled it
- Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.European radiology · 2020Pooled it
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- 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
- Evaluating the grade and IDH mutation status of gliomas by using intravoxel incoherent motion (IVIM) MRI and DSC perfusion MRI.BMC medical imaging · 2026Article
- IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.Diagnostics (Basel, Switzerland) · 2026Article
- Externally validated explainable 3D CNN ensemble model for non-invasive prediction of IDH and MGMT status in gliomas.Frontiers in oncology · 2026Article
- Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- The Value of Cerebral Blood Volume Derived from Dynamic Susceptibility Contrast Perfusion MRI in Predicting IDH Mutation Status of Brain Gliomas-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Plasma-Derived Exosomal i-tRF-LeuCAA as Biomarker for Glioma Diagnosis and Promoter of Epithelial-Mesenchymal Transition via TPM4 Regulation.CNS neuroscience & therapeutics · 2025Article
- A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.PloS one · 2025Article
- A multitask framework based on CA-EfficientNetV2 for the prediction of glioma molecular biomarkers.Frontiers in neurology · 2025Article
- Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas.Frontiers in oncology · 2025Article
- Review
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3 authors.
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Abstract
backgroundThe aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI.
methodsFour hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2* susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model.
resultsThe IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2* susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype.
conclusionsWe developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.
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