AI Assessment of Heterogeneous Histopathologic Brain Tumor Sub-regions: the RANO MICCAI BraTS-Path 2024 Challenge.

BACKGROUND: Automated classification of glioblastoma (IDH-wt, WHO CNS Gr.4) histopathologic sub-regions in whole slide images (WSI) is essential for quantitative tumor characterization, but faces substantial challenges from intra-tumoral heterogeneity and inter-observer annotation variability. The AI-RANO & RANO-Pathology working groups organized BraTS-Path 2024, representing the first international benchmarking environment and publicly available spatially annotated dataset, towards promoting the development and fair evaluation of Artificial Intelligence (AI) models to classify specific sub-regions: cellular tumor, pseudopalisading necrosis (PN), microvascular proliferation, geographic necrosis, infiltration into the cortex, and white matter penetration (WM). METHODS: Multi-institutional H&E-stained WSIs had reference standard samples generated by eight expert neuropathologists, following a rigorous peer review protocol, resulting in 309,118 data samples (training/validation/hold-out testing: 96,485/62,387/150,246). Quantitative performance evaluation of six AI approaches was conducted based on multiple metrics, including Accuracy and Matthew's correlation coefficient (MCC). A robust statistically-aware ranking strategy offered statistical significance of the final rankings. RESULTS: Top-performing approaches employed common strategies, including large pre-trained models, ensembling, and extensive data augmentation, reporting Accuracy=0.69 and MCC=0.58. Key challenges included severe class imbalance, hampering minority class performance, and inter-annotator variability, especially for ambiguous sub-regions such as PN or WM. CONCLUSIONS: BraTS-Path represents a valuable public resource for glioblastoma histopathologic sub-region assessment. Our findings indicate the specific task remaining an open research problem, highlighting the influence of rare sub-regions, the absence of a consistent "gold standard" annotation protocol, and the potential of foundation models to improve generalization, guiding future research toward developing more reliable AI tools for quantitative neuropathology.