Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics
The addition of Δ ADC improves the clinical model, highlighting the potential complementary value of radiomic features in prognostic modeling.
Authors
- Frederik De Keyzer (ORCID: https://orcid.org/0000-0002-1855-3274)
- Sarah Deschuymer (ORCID: https://orcid.org/0000-0002-2271-6566)
- Rüveyda Dok (ORCID: https://orcid.org/0000-0002-5456-814X)
- Vincent Vandecaveye
- Annouschka Laenen (ORCID: https://orcid.org/0000-0002-1371-442X)
- Sandra Nuyts (ORCID: https://orcid.org/0000-0002-5540-4796)
- Johannes Devos (ORCID: https://orcid.org/0000-0002-6623-3769)
- Heleen Bollen (ORCID: https://orcid.org/0000-0002-3612-2689)
Institutions
- Statistics Belgium (BE)
- VIB-KU Leuven Center for Cancer Biology (BE)
- KU Leuven (BE)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.phro.2025.100759
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00