PATH-39. AI-BASED IDENTIFICATION OF GLIOMA IDH MUTATIONAL STATUS FROM H&E-STAINED WHOLE SLIDE IMAGES

Abstract INTRODUCTION Isocitrate-dehydrogenase (IDH) mutational status is diagnostically critical in adult gliomas, with prognostic and therapeutic implications. IDH status cannot be determined by sole histologic assessment and requires molecular testing. We hypothesize that AI-based analysis can accurately predict IDH mutational status in glioma, by retrieving sub-visual cues in H&E-stained digitized tissue sections. MATERIALS AND METHODS A retrospective discovery cohort of 1,534 cases (756/778 IDH-wildtype/IDH-mutant, including all available grades) from the TCGA-LGG/TCGA-GBM collections was used for model development with 10-fold cross-validation (CV). A replication cohort of 114 cases (82/32 IDH-wildtype/IDH-mutant) from the University of Pennsylvania Health System (UPHS) was used for independent hold-out evaluation. Each 20X magnification whole slide image (WSI) underwent comprehensive curation for elimination of artifactual content, followed by partitioning into 256x256 patches, and feature extraction using pre-trained i) ImageNet weights (baseline), and ii) self-supervised vision transformer (ViT). A weakly-supervised attention-based multiple-instance-learning framework distinguished cases as IDH-wildtype or IDH-mutant, while generating visually interpretable attention heatmaps. RESULTS Evaluation on discovery and replication cohorts with the ViT yield accuracy of 90.84% (AUCDiscovery=97.16%) and 93.94% (AUCReplication=97.36%), respectively, with high sensitivity (i.e., high confidence in correctly predicting IDH-wildtype glioma). Comparison of the ViT with the baseline model on the replication cohort indicates the ViT superiority by improvements of 8% and 9.5% in accuracy and AUC, respectively. Histologic assessment of heatmaps indicates IDH-wildtype tumors exhibiting distinct regions of significant pleomorphism and microvascular proliferation, while IDH-mutant tumors exhibit dense nodular cell concentrations, microcystic architecture, and gemistocytic cells. CONCLUSION Our accurate H&E-based computational determination of glioma IDH status, with interpretations aligned with human-identifiable features, can contribute to obviating the need for molecular analyses and enable expedited diagnosis even in community healthcare settings. This study robustly identifies morpho-IDH correlates and could enable expedited patient prognostic stratification.