Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas
BRAF status can be assessed non-invasively using ML models based on whole-brain FLAIR sequences. Dependence on inconsistent manual or automated segmentations can be reduced by integrating tumor region information into the model through pretraining.
Authors
- Uri Tabori (ORCID: https://orcid.org/0000-0002-5019-2683)
- Birgit Ertl‐Wagner (ORCID: https://orcid.org/0000-0002-7896-7049)
- Farzad Khalvati (ORCID: https://orcid.org/0000-0001-5616-8660)
- Kareem Kudus
- Anthony Liu (ORCID: https://orcid.org/0000-0002-1374-2455)
- Peter B. Dirks (ORCID: https://orcid.org/0000-0001-5718-6465)
- Matthias Wagner (ORCID: https://orcid.org/0000-0001-6501-839X)
- Cynthia Hawkins (ORCID: https://orcid.org/0000-0003-2618-4402)
- Min Sheng (ORCID: https://orcid.org/0000-0002-2307-8607)
- Julie Bennett
Institutions
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- University Hospital Augsburg (DE)
- Princess Margaret Cancer Centre (CA)
- SickKids Foundation (CA)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1038/s43856-025-01204-y
- Citations
- 1
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- Canadian Institutes of Health Research