Keywords: Diagnosis/Prediction, Radiomics, C5aR1; high-grade glioma; MRI; prognosis; biomarker
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.
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