CT Radiomic Features Are Associated with DNA Copy Number Alterations of Head and Neck Squamous Cell Carcinomas
CNA subgroups exhibit distinct radiomic phenotypes, primarily reflected in texture and intensity characteristics. These findings enhance our understanding of the biologic significance of radiomic information in HNSCC. In the clinical setting, as CNA-positive and -negative HNSCCs may emerge as distinct subclasses with unique staging schemes and treatment implications, improved CT radiomics-based prediction models could offer a noninvasive, cost-effective method for CNA subtyping.
Authors
- Andrea Schreier (ORCID: https://orcid.org/0000-0002-2241-3119)
- Benedikt Paul
- Philipp Baumeister (ORCID: https://orcid.org/0000-0003-2759-4737)
- Seyedmehdi Payabvash (ORCID: https://orcid.org/0000-0003-4628-0370)
- Stefan P. Haider (ORCID: https://orcid.org/0000-0002-7640-3645)
- Tal Zeevi (ORCID: https://orcid.org/0000-0001-8917-7061)
- Kariem Sharaf (ORCID: https://orcid.org/0000-0001-7799-4005)
- Christoph Reichel (ORCID: https://orcid.org/0009-0001-8145-2767)
- Moritz Gross (ORCID: https://orcid.org/0000-0002-6831-5004)
- Martin Canis
- Jasmin Krenn
Institutions
- University of Cologne (DE)
- Columbia University Irving Medical Center (US)
- Yale University (US)
- Charité - Universitätsmedizin Berlin (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-10-09
- DOI
- https://doi.org/10.3174/ajnr.a9029
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00