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<0.001), and further pixel-wise validation on 828 separate annotations showed high overlap between predicted and ground truth segmentations (precision=0.88, recall= 0.85, F1-score=0.87). We then identified a longitudinal cohort of 115 newly diagnosed glioblastoma tissue paired with the recurrence post-treatment (RT/TMZ), including all 827 H&E whole slide images scanned at 40x. The quantification of longitudinally paired tissue showed a relative increase in geographic necrosis (+62%) and mild infiltration (+110%) and a relative decrease in palisading necrosis (-54%) and heavy infiltration (-35%) between the two timepoints. Patients with rapid clinical progression had more areas of large geographic necrosis (p=0.002), and mesenchymal tissue increase was associated with worse overall survival (p=0.016). Digitally quantified heavy infiltration as a predominant tissue on recurrence outperformed corresponding pathologist estimation of tumor as a predominant tissue on recurrence in stratifying overall-survival (p=0.005 vs p=0.640). Collectively, our AI-powered classifier enables an automated objective quantification of glioblastoma tissue, identifies markers of clinical response, and offers a potential adjunct to enhance routine pathologic reporting of glioblastoma.