A novel family model for dose prediction in esophageal cancer VMAT planning
The novel artificial intelligence approach holds promise in providing medical physicists with valuable guidance for VMAT planning optimization.
Authors
- Meng Fan (ORCID: https://orcid.org/0000-0002-3761-4431)
- Qifeng Wang (ORCID: https://orcid.org/0000-0002-3036-6084)
- Xiaohuan Sun
- Jing Cai (ORCID: https://orcid.org/0000-0001-5747-9399)
- Jie Gong (ORCID: https://orcid.org/0000-0002-3923-2488)
- Ge Ren (ORCID: https://orcid.org/0000-0003-2049-2682)
- Jie Li (ORCID: https://orcid.org/0000-0002-1558-8623)
- Hongfei Sun (ORCID: https://orcid.org/0000-0002-3995-7251)
- Wei Huang (ORCID: https://orcid.org/0009-0007-8139-3596)
- Lina Zhao (ORCID: https://orcid.org/0000-0002-0011-6471)
- Zhongfei Wang (ORCID: https://orcid.org/0000-0002-9584-2678)
- Jiarui Zhu (ORCID: https://orcid.org/0000-0001-6225-8910)
- Yufen Liu (ORCID: https://orcid.org/0000-0002-1693-0339)
Institutions
- Hong Kong Polytechnic University (HK)
- University of Electronic Science and Technology of China (CN)
- Shandong Tumor Hospital (CN)
- Sichuan Cancer Hospital (CN)
- University of Hong Kong - Shenzhen Hospital (CN)
- Xijing Hospital (CN)
- Shandong First Medical University (CN)
- University of Hong Kong (HK)
- Air Force Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18059
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China