External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables
To our knowledge, this is the first study to externally validate a multimodality DL-based ORN NTCP model. Utilising mandible dose distribution maps, these models show promise for enhancing spatial risk assessment and guiding dental and oncological decision-making, though further research is essential to address overfitting and domain shift for reliable clinical use.
Authors
- Vinod Patel (ORCID: https://orcid.org/0000-0002-3709-5619)
- Andrew P. King (ORCID: https://orcid.org/0000-0002-9965-7015)
- Teresa Guerrero Urbano (ORCID: https://orcid.org/0000-0003-3090-541X)
- Christian Rønn Hansen (ORCID: https://orcid.org/0000-0001-5716-6069)
- Jørgen Johansen (ORCID: https://orcid.org/0000-0003-3911-3929)
- Laia Humbert-Vidan
Institutions
- University of Southern Denmark (DK)
- King's College London (GB)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Odense University Hospital (DK)
- London Cancer (GB)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.phro.2024.100668
- Citations
- 1
- Primary Topic
- Oral health in cancer treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- Nvidia
- Cancer Research UK
- Novo Nordisk Fonden