Multiomic random forest toxicity modeling of radiation esophagitis
All multiomic models outperformed the base model. The combined radiomic-dosiomic model provides novel insights into high-grade RE risk and may inform future strategies for toxicity mitigation and personalized treatment planning.
Authors
- Laurence E. Court (ORCID: https://orcid.org/0000-0002-3241-6145)
- S. Nair (ORCID: https://orcid.org/0009-0001-0371-635X)
- Ramon M. Salazar (ORCID: https://orcid.org/0000-0002-1883-9029)
- Joshua S. Niedzielski (ORCID: https://orcid.org/0000-0002-7666-5039)
- Ting Xu (ORCID: https://orcid.org/0000-0002-7851-3389)
- Zhongxing Liao (ORCID: https://orcid.org/0000-0002-2224-9478)
- Alexandra O. Leone (ORCID: https://orcid.org/0000-0002-2497-7209)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.phro.2025.100838
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00