CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer
Question There is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer. Findings Clinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone. Clinical relevance SHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.
Authors
- Clara Alfaro‐Cervelló (ORCID: https://orcid.org/0000-0002-3737-4028)
- Delfina Dualde-Beltrán
- A. Campos Nogué
- María Maroñas
- Ángel Alberich‐Bayarri (ORCID: https://orcid.org/0000-0002-5932-2392)
- Ana Jiménez-Pastor (ORCID: https://orcid.org/0000-0002-0978-9429)
- Fuensanta Bellvís
- G. Bruixola (ORCID: https://orcid.org/0000-0003-3229-0770)
- Almudena Fuster-Matanzo (ORCID: https://orcid.org/0000-0001-8667-4011)
- Nuria Grimalt
- Andrés Cervantes (ORCID: https://orcid.org/0000-0003-3806-3691)
- Vicente Escorihuela
- María Eugenia Iglesias
- Noelia Tarazona
- Nader Salhab-Ibáñez
Institutions
- Cervantes Institute (ES)
- Universitat de València (ES)
- Instituto de Salud Carlos III (ES)
- Hospital Clínico Universitario de Valencia (ES)
- Quantitative BioSciences (US)
- INCLIVA Health Research Institute (ES)
- Centro de Investigación Biomédica en Red de Cáncer (ES)
Publication Details
- Journal
- European Radiology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1007/s00330-024-11301-6
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62