Abstract #0401

Radiomics profiling identifies the incremental value of MRI features to key molecular biomarkers for risk stratification of high-grade gliomas

Guoqiang Yang¹, Shuaitong Zhang², Xiaochun Wang¹, Yan Tan¹, Jingwei Wei², Xiaoxu Chen³, Jie Tian², and Hui Zhang¹

¹Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi Province, China, ²Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China, ³School of Economics and Management, Shanxi University, Taiyuan, Shanxi Province, China

To identify the incremental value of MRI features to the key molecular biomarkers for risk stratification of high-grade gliomas (HGGs). A comprehensive radiomics analysis integrated MRI features, clinical characteristics and genetic information was performed on 137 patients from TCGA/TCIA dataset and our institution. The combined model integrated radiomics signature with age and IDH genotype holds the best prognostic value. The radiomics signature has incremental prognostic value beyond the key molecular biomarkers, and could identify risk subgroups in various clinical and molecular subgroups. Our comprehensive radiomics analysis provided a potential tool to guide an individual diagnosis and treatment decisions for HGGs.

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