PATH-50. AI-POWERED AUTOMATED TISSUE SEGMENTATION IMPROVES OUTCOME STRATIFICATION IN GLIOBLASTOMA

Abstract Glioblastoma displays morphological heterogeneity on routine H&E whole slide images, with conflicting evidence on the reliability of morphological features in predicting tumor behavior and treatment response. However, current standards for assessing morphology and disease status rely on nonquantitative manual evaluation by pathologists, which are prone to inherent variation. We sought to (1) expand the tools available for quantitative assessment and (2) evaluate the utility of this approach in identifying valuable biomarkers. We trained a deep learning-based algorithm using DenseNet on HALO AI platform for automated segmentation of six tissue classes (heavy infiltration, mild infiltration, mesenchymal tissue, geographic necrosis, palisading necrosis, and hemorrhage) on 3232 labeled annotations from 100 whole slide images. Manual pathologists’ estimations from clinical records showed strong correlation with corresponding digital quantifications (p