PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer
The basic DenseNet architecture with 81 layers demonstrated a predictive performance on par with SOTA models featuring more intricate architectures in the internal test set, and better performance in the external test. The late fusion of CT and PET imaging data yielded superior performance in the external test.
Authors
- Johannes A. Langendijk (ORCID: https://orcid.org/0000-0003-1083-372X)
- Nanna M. Sijtsema
- Stefan Both (ORCID: https://orcid.org/0000-0001-6652-6054)
- Baoqiang Ma (ORCID: https://orcid.org/0000-0002-6461-1244)
- 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)
- Jiapan Guo (ORCID: https://orcid.org/0000-0003-3966-4405)
Institutions
- University Medical Center Groningen (NL)
- University of Groningen (NL)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1016/j.radonc.2025.110852
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- Rijksuniversiteit Groningen
- China Scholarship Council