ArticleCancers2021
Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.Frontiers in oncology · 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
- Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.Journal of imaging · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- Review
- Updates in the management of intradialytic hypotension: Emerging strategies and innovations.World journal of nephrology · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Role of dynamic contrast-enhanced and dynamic susceptibility contrast imaging in evaluating the biological features of glioma.Quantitative imaging in medicine and surgery · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Development and verification of a radiomics model to forecast Ki67 index and prognosis in advanced gastric tubular adenocarcinoma.BMC gastroenterology · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Update report on the quality of gliomas radiomics: An integration of bibliometric and radiomics quality score.World journal of radiology · 2024Article
- Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- Machine Learning-Based Prediction of GliomaCancers · 2024Article
- Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.Journal of imaging informatics in medicine · 2024Article
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
- Hyperpolarized δ-[1-Scientific reports · 2023Article
- Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update.International journal of molecular sciences · 2023Review
- Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen's kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen's kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH-wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC-MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology.
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