Developing a novel dosiomics model to predict treatment failures following lung stereotactic body radiation therapy
The dosiomics model integrating the interaction between CT and Dose can effectively predict treatment failure following lung SBRT treatment and may serve as a useful tool to proactively evaluate and select lung SBRT treatment plans to reduce treatment failure in the future.
Authors
- Michael James Baine
- Abhishta Bhandari (ORCID: https://orcid.org/0000-0002-4509-9257)
- Kyuhak Oh (ORCID: https://orcid.org/0000-0003-3609-6430)
- Kurtis C. Johnson (ORCID: https://orcid.org/0000-0002-4862-5865)
- Linda My Huynh (ORCID: https://orcid.org/0000-0002-8655-8587)
- S. Zhou (ORCID: https://orcid.org/0000-0002-7517-9653)
- Sarah Wisnoskie
- Chi Lin (ORCID: https://orcid.org/0000-0002-7279-0473)
- Shuo Wang (ORCID: https://orcid.org/0000-0002-0990-639X)
- Chi Zhang (ORCID: https://orcid.org/0000-0002-0463-5260)
- Yu Lei (ORCID: https://orcid.org/0000-0002-8067-3015)
- Fang Yu (ORCID: https://orcid.org/0000-0001-9913-1331)
Institutions
- Novant Health (US)
- University of Nebraska Medical Center (US)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-12-12
- DOI
- https://doi.org/10.3389/fonc.2024.1438861
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Cancer Institute