Combined model significantly improves survival risk stratification in GBM patients, indicating clinical relevance.
Motivation: GBM is associated with low survival rates and poor prognoses. Accurately stratifying survival risk is crucial for personalized treatment strategies. Goal(s): Aimed to construct an accurate survival risk stratification model by integrating radiomics and clinical factors, and explore its biological foundations. Approach: We collected clinical and MRI data from 369 patients and constructed combined models for survival risk stratification, and explored the biological foundations. Results: The combined model achieved a C-index of 0.815 for survival risk stratification, significantly outperforming the individual radiomics and clinical models. The activation of neurotransmitter and lipid metabolism pathways may be associated with high-risk glioblastomas. Impact: The combined model improves survival risk stratification in IDH-wt GBM by integrating radiomics and clinical factors, supporting the guidance of personalized treatment strategies. The biological foundations may provide new insights for future therapies.
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Li et al. (2025) studied this question.
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