Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model
An offline ART regimen utilizing kV iCBCT and UNet++ based deep learning model is clinically feasible for lung cancer treatment. This approach provides enhanced image quality, comparable treatment plans to pCT, and precise dose delivery.
Authors
- Hongwei Zeng (ORCID: https://orcid.org/0000-0003-0714-5539)
- Minghe Lv (ORCID: https://orcid.org/0000-0001-9342-773X)
- E Xiangyu (ORCID: https://orcid.org/0009-0004-1922-9291)
- Ruping Zhao (ORCID: https://orcid.org/0000-0003-1096-6717)
- Jingping Yu (ORCID: https://orcid.org/0000-0003-4383-6222)
- Yang Zhang (ORCID: https://orcid.org/0000-0003-2797-7535)
- Shaobin Wang (ORCID: https://orcid.org/0000-0002-0377-2095)
- Su Zeng (ORCID: https://orcid.org/0000-0002-1418-6825)
- Wenhao Shen (ORCID: https://orcid.org/0000-0002-4410-128X)
- Qi Chen (ORCID: https://orcid.org/0000-0003-2663-9489)
- Yue Feng (ORCID: https://orcid.org/0000-0003-3407-8992)
- Wenhui Guan
Institutions
- Shanghai University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Shuguang Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1002/acm2.14582
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00