Predicting the IDH1 Mutation Status of Gliomas based on Multi-modality MRI Radiomics Combined with VASARI Features

Motivation: The IDH1 mutant state is an independent risk factor of affecting the treatment and prognosis of glioma. Predicting the IDH1 status accurately pre-operator is crucial for making personalized treatment decisions for glioma patients. Goal(s): This study aims to propose a non-invasive and convenient model based on MRI to predict the IDH1 status of gliomas before operation accurately. Approach: Building three machine learning models based on multi-sequence MRI radiomics features, VASARI features, and combined features to predict the IDH1 status. Results: These three models can predict the IDH1 status effectively and accurately, the combined model has the best diagnostic performance. Impact: Models based on conventional MRI sequences and VASARI features provide the clinical value for evaluation of molecular typing in gliomas. It is expected to become a practical tool for the non-invasive characterization of gliomas to help the individualized treatment planning.