AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides
Determining glioma diagnosis directly from H&E slides can obviate the need for molecular profiling, expedite conclusive diagnosis, and, hence, clinical decision-making. These findings motivate the development of advanced domain-relevant FMs and the design of more adaptable slide-level aggregation techniques.
Authors
- Shubham Innani (ORCID: https://orcid.org/0000-0003-3616-0308)
- Spyridon Bakas (ORCID: https://orcid.org/0000-0001-8734-6482)
- Bhakti Baheti (ORCID: https://orcid.org/0000-0001-5475-3903)
- William R. Bell (ORCID: https://orcid.org/0000-0001-6326-3885)
- MacLean P Nasrallah
Institutions
- Indiana University Health (US)
- Emory University (US)
- Indiana Cancer Consortium (US)
- Neurological Surgery (US)
- Indiana University Melvin and Bren Simon Comprehensive Cancer Center
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Neuro-Oncology
- Published
- 2025-08-07
- DOI
- https://doi.org/10.1093/neuonc/noaf189
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36