Preoperative noninvasive prediction of IDH mutation status is crucial for prognosis and therapeutic decision making. In this study, we evaluated the qualitative and quantitative MRI features, namely, Visually Accessible Rembrandt Images (VASARI) features and apparent diffusion coefficient radiomics features in identifying IDH1 mutation status in lower-grade gliomas (WHO grade II-III). Results by machine learning methods showed that the combination achieved a better prediction performance. Our model may have the potential to serve as an alternative to the conventional workflow for the noninvasive identification of the molecular profiles.
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