Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials
This prospective trial-based study reveals an overall limited predictive capacity of radiomics and dosiomics models and a large influence of radiation technique. IMRT-specific models should be investigated further.
Authors
- Kim Melanie Kraus (ORCID: https://orcid.org/0000-0003-3359-2316)
- Jan Caspar Peeken (ORCID: https://orcid.org/0000-0003-2679-9853)
- Danai Pletzer
- Lukas M. Reuter
- Julia A. Schnabel
- Denise Bernhardt
- Stephanie E. Combs
- Stefan M. Fischer
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- TUM Klinikum (DE)
- Helmholtz Zentrum München (DE)
Publication Details
- Journal
- International Journal of Radiation Oncology*Biology*Physics
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1016/j.ijrobp.2026.01.031
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Deutsche Forschungsgemeinschaft
- Deutschen Konsortium für Translationale Krebsforschung
- National Cancer Institute