ArticleFrontiers in neurology2025
Deep learning and pathomics analyses predict prognosis of high-grade gliomas.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Prognostic and predictive determinants in high-grade gliomas: integrating tumor-intrinsic biology with patient and system-level factors.Frontiers in neurology · 2025Review
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15 authors.
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Abstract
Objective: Utilizing pathomics to analyze high-grade gliomas and provide prognostic insights. Methods: Regions of Interest (ROIs) in tumor areas were identified in whole-slide images (WSI). Tumor patches underwent cropping, white space removal, and normalization. A deep learning model trained on these patches aggregated predictions for WSIs. Pathological features were extracted using Pearson correlation, univariate Cox regression, and LASSO-Cox regression. Three models were developed: a Pathomics-based model, a clinical model, and a combined model integrating both. Results: Pathological and Clinical Features were used to build two models, leading to a predictive model with a C-index of 0.847 (train) and 0.739 (test). High-risk patients had a median progression-free survival (PFS) of 10 months (p<0.001), while low-risk patients had not reached median PFS. Stratification by IDH status revealed significant PFS differences. Conclusion: The combined model effectively predicts high-grade glioma prognosis.
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