Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma.

Motivation: High-grade glioma is a complex disease characterized by genome instability caused by the accumulation of genetic alterations. Identifying and evaluating the oncogenes involved is crucial for determining treatment strategies and evaluating prognosis. Goal(s): We sought to explore whether radiomics models based on MRI features can noninvasively predict C5aR1 expression and the prognosis of patients with high-grade glioma. Approach: This study uses machine learning approaches based on paired MRI and RNA sequencing data. Results: The radiomics models yield satisfactory performances in predicting C5aR1 expression. Our findings also reveal associations between MRI radiomics and immune-related features. Impact: As an effective and reproducible tool, our radiomics model may support clinical decision making and individualized treatment.