Abstract #3776

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

Xiaohua Chen^1,2, Zhiqiang Chen³, Shili Liu¹, Ruodi Zhang¹, Yunshu Zhou¹, Yuhui Xiong⁴, and Aijun Wang⁵

¹Clinical medicine school of Ningxia Medical University, Yinchuan, China, ²Medical Imaging Center of Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China, ³Department of Radiology ,the First Hospital Affiliated to Hainan Medical College, Haikou, China, ⁴GE Healthcare MR Research, Beijing, China, ⁵Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics, Gliomas

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.

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