External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer
Deep learning-derived ΔPET-GTV may support clinically meaningful assessment of post-treatment disease status and risk stratification, offering a scalable alternative to manual segmentation in PET/CT follow-up.
Authors
- Marcel van Herk (ORCID: https://orcid.org/0000-0001-6448-898X)
- Eliana Vásquez Osorio (ORCID: https://orcid.org/0000-0003-0741-994X)
- Andrew McPartlin (ORCID: https://orcid.org/0000-0002-4101-3276)
- Flemming Littrup Andersen
- Barbara Malene Fischer (ORCID: https://orcid.org/0000-0002-6065-3375)
- Claes Nøhr Ladefoged (ORCID: https://orcid.org/0000-0002-3583-4190)
- Marianne Aznar (ORCID: https://orcid.org/0000-0001-6452-0310)
- Azadeh Abravan (ORCID: https://orcid.org/0000-0003-4839-6705)
- James Price
- David Gergely Kovacs Petersen (ORCID: https://orcid.org/0000-0002-0383-1446)
- Iskandar Mohamed
Institutions
- University of Copenhagen (DK)
- University of Toronto (CA)
- Copenhagen University Hospital (DK)
- Princess Margaret Cancer Centre (CA)
- Rigshospitalet (DK)
- University of Manchester (GB)
- Manchester University NHS Foundation Trust (GB)
- Cancer Research UK Manchester Institute (GB)
- The Christie NHS Foundation Trust (GB)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Acta Oncologica
- Published
- 2025-08-30
- DOI
- https://doi.org/10.2340/1651-226x.2025.43977
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cancer Research UK
- National Institute for Health and Care Research
- Hartmann Fonden
- Manchester Biomedical Research Centre