MRI radiomic features to predict idh1 mutation status in gliomas: A machine learning approach using gradient tree boosting

Keyphrases

• Glioma 100%
• Magnetic Resonance Imaging 100%
• Mutation Status 100%
• Machine Learning Approach 100%
• Isocitrate Dehydrogenase 1 (IDH1) 100%
• Radiomics 100%
• Gradient Boosted Trees 100%
• Fluid-attenuated Inversion Recovery (FLAIR) 50%
• Receiver Operating Characteristic Curve 50%
• Diffusion-weighted Imaging 50%
• F1 Score 50%
• Intraoperative Magnetic Resonance Imaging (iMRI) 50%
• Isocitrate Dehydrogenase 1 mutation 50%
• Area under the Curve 33%
• XGBoost Model 33%
• Multiparametric Radiomics 33%
• Recent Advances 16%
• Machine Learning 16%
• Prognostic Indicator 16%
• Disease Process 16%
• Training Set 16%
• Stratified Sampling 16%
• Machine Learning Analysis 16%
• Synthetic Minority Oversampling Technique (SMOTE) 16%
• Hyperparameters 16%
• XGBoost Classifier 16%
• Accuracy Score 16%
• Combined Water 16%
• Extreme Gradient Boosting (XGBoost) Classifier 16%

Neuroscience

• Magnetic Resonance Imaging 100%
• Isocitrate Dehydrogenase 1 100%
• Diffusion MRI 50%