Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer
DL-based NTCP models performed better than the reference model when validated in data from the same institute. Improved performance in the external data set was achieved with transfer learning, demonstrating the need for multicenter training data to realize generalizable DL-based NTCP models.
Authors
- Johannes A. Langendijk (ORCID: https://orcid.org/0000-0003-1083-372X)
- Nanna M. Sijtsema
- Peter M. A. van Ooijen (ORCID: https://orcid.org/0000-0002-8995-1210)
- Lisanne V. van Dijk (ORCID: https://orcid.org/0000-0002-9515-5616)
- Clifton D. Fuller (ORCID: https://orcid.org/0000-0002-5264-3994)
- Roel J.H.M. Steenbakkers
- Hung Chu (ORCID: https://orcid.org/0000-0002-9531-8980)
- Amy C. Moreno (ORCID: https://orcid.org/0000-0001-6762-6807)
- Suzanne P.M. de Vette (ORCID: https://orcid.org/0000-0003-3243-2898)
- Hendrike Neh
Institutions
- University Medical Center Groningen (NL)
- The University of Texas MD Anderson Cancer Center (US)
- University of Groningen (NL)
Publication Details
- Journal
- International Journal of Radiation Oncology*Biology*Physics
- Published
- 2024-08-13
- DOI
- https://doi.org/10.1016/j.ijrobp.2024.07.2334
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- National Science Foundation
- Kansas NSF EPSCoR
- Dutch Arthritis Society
- Rijksuniversiteit Groningen
- ZonMw
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- KWF Kankerbestrijding
- Universitair Medisch Centrum Groningen
- Nihon Superior
- National Institutes of Health
- University of Texas MD Anderson Cancer Center
- NIHR BioResource
- National Cancer Institute
- National Institute of Biomedical Imaging and Bioengineering
- National Institute of Dental and Craniofacial Research