acePrognostic risk stratification in glioma based on the foundation pathology model with identification of key genes and biological pathways associated with prognosis: a multicenter study
Abstract Purpose: Artificial intelligence(AI)-based feature extraction from whole slide images (WSIs) shows significant promise for glioma prognosis. However, while foundation pathology models have potential, their features typically lack biological interpretability and clear links to disease mechanisms, limiting their clinical translational value. Methods: We propose a clustering Vision Transformer (ViT)–based framework for pathological image analysis. Tens of thousands of image patches extracted from each WSI…
An AI model using clustered pathology slide features produced risk scores tied to glioma survival and improved a clinical-molecular nomogram, while also pointing to risk-associated genes and pathways.
Evidence
- Peer-reviewedActa Neuropathologica Communications2026-10-24
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Truvace Impact Record TRV-2026-2259, v1: “Prognostic risk stratification in glioma based on the foundation pathology model with identification of key genes and biological pathways associated with prognosis: a multicenter study.” Truvace, 2026-10-26. /record/TRV-2026-2259 (accessed at citation time). sha256 fe159fd7e6e6ef9d…
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