Machine learning-based radiopathomics ensemble model for predicting multiple molecular subtypes of adult diffuse glioma: a multicenter retrospective study.

Abstract excerpt

BACKGROUND: IDH and TERTp mutations serve as key molecular markers in glioma, closely associated with tumor clinical behavior, treatment response, and patient survival. Precise identification of molecular subtypes facilitates improved clinical risk stratification. This study aims to develop an integrated radiomics and pathomics model for the rapid classification of glioma molecular subtypes. METHODS: This...

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• Brain Neoplasms
• Glioma
• Isocitrate Dehydrogenase
• Machine Learning
• Predictive Learning Models