3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.

ABSTRACT: Advances in digital pathology and machine learning have the potential to revolutionize diagnostic neuropathology. Current brain tumor models are typically trained and validated using morphologic features from a single hematoxylin and eosin (H&E)-stained slide per patient. Yet, brain tumors such as diffuse glioma are known for their epigenetic, genetic, and transcriptional heterogeneity within an individual patient. The impact of this heterogeneity on model accuracy and development is unknown. To quantitatively investigate morphologic intratumoral heterogeneity in glioblastoma (GBM), we acquired 92 regionally distinct samples representing maximal tumor sampling across 10 patients with isocitrate dehydrogenase-wildtype GBM and quantified cell density, nucleus area, and nucleus circularity fr

SUBMITTER: Voong V 

PROVIDER: S-EPMC12531486 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature