Machine Learning-Based Classification of IDH Mutant Gliomas Using VASARI and Radiomics Features

Motivation: Diagnosis of molecular subtypes of IDH-mutant gliomas on MRI has presented a challenge in clinical practice. Goal(s): To classify IDH-mutant gliomas we compared different ML models using qualitative and quantitative features from preoperative MRI. Approach: Three models were compared, using only qualitative VASARI features as scored by two blinded neuroradiologists, the other used quantitative features from FLAIR and T1Gd and finally combining both in a third model. Results: The VASARI feature based model showed moderate diagnostic accuracies for different tumor entities, which was higher than the Radiomics only model. Combining both features improved results, emphasizing the importance of feature selection in clinical applications. Impact: This study demonstrates the potential of machine learning models in enhancing the accuracy of IDH-mutant glioma classification on preoperative MRI images.