Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach
The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.
Authors
- Katharina Geißler (ORCID: https://orcid.org/0000-0002-4271-5878)
- Nadja Ziller (ORCID: https://orcid.org/0000-0003-1548-4741)
- Tiago Azevedo (ORCID: https://orcid.org/0000-0002-2052-3832)
- Thomas Bitter
- David Pertzborn (ORCID: https://orcid.org/0000-0002-0230-7765)
- Sebastian Wolter
- Píetro Lió (ORCID: https://orcid.org/0000-0002-0540-5053)
- Anna Mühlig (ORCID: https://orcid.org/0009-0001-8278-6494)
- Mussab Kouka (ORCID: https://orcid.org/0000-0002-4822-907X)
- Daniela Pelzel
- Orlando Guntinas‐Lichius (ORCID: https://orcid.org/0000-0001-9671-0784)
- Ferdinand von Eggeling (ORCID: https://orcid.org/0000-0002-8062-6999)
- Ayman Bali
- Anna Xylander
- Günther Ernst
- Ulrike Weyer
Institutions
- University of Cambridge (GB)
- Comprehensive Cancer Center Mainfranken (DE)
- Jena University Hospital (DE)
Publication Details
- Journal
- Cancers
- Published
- 2025-05-10
- DOI
- https://doi.org/10.3390/cancers17101617
- Citations
- 5
- Primary Topic
- Optical Imaging and Spectroscopy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Universitätsklinikum Jena
- HORIZON EUROPE Framework Programme