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AI Assessment of Heterogeneous Histopathologic Brain Tumor Sub-regions: the RANO MICCAI BraTS-Path 2024 Challenge
- Truvace record
- TRV-2026-2862raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 0733dd6bca29…check it
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 evalua…
The problem
In the BraTS-Path 2024 challenge of six AI approaches on glioblastoma slide images, the top performers reached only 0.69 accuracy and 0.58 MCC, with rare sub-regions such as pseudopalisading necrosis and white matter penetration classified poorly.
What this doesn’t fix
The authors say the task remains an open research problem, with severe class imbalance and no consistent gold-standard annotation protocol limiting reliability for ambiguous sub-regions.
Sources
1 on file- Neuro-Oncologydoi:10.1093/neuonc/noag283
Cite this record
v1Truvace Impact Record TRV-2026-2862, v1: “AI Assessment of Heterogeneous Histopathologic Brain Tumor Sub-regions: the RANO MICCAI BraTS-Path 2024 Challenge.” Truvace, 2026-11-12. /record/TRV-2026-2862 (accessed on the day you copy it). sha256 0733dd6bca294458…
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