Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer
The proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.
Authors
- Yichao Shen (ORCID: https://orcid.org/0000-0002-5257-6808)
- Yongqiang Zhou (ORCID: https://orcid.org/0000-0002-7199-2321)
- Xiance Jin (ORCID: https://orcid.org/0000-0002-4117-5953)
- Li Shao (ORCID: https://orcid.org/0000-0002-3172-9879)
- Weiqian Huang
- Ji Zhang (ORCID: https://orcid.org/0000-0002-3835-7975)
- Huan Liu (ORCID: https://orcid.org/0000-0002-2676-6982)
- Dong Wang (ORCID: https://orcid.org/0000-0001-6267-8819)
- Ting Liu (ORCID: https://orcid.org/0000-0002-3025-0276)
- Weihua Ni
- Ziqing Xiang
- Ce Han
- Xianwen Yu
- Wen Fu
Institutions
- Nantong University (CN)
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- The Affiliated Taizhou People's Hospital of Nanjing Medical University (CN)
- Taizhou Central Hospital (CN)
- Zhejiang Taizhou Hospital (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1002/acm2.70353
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Wenzhou Municipal Science and Technology Bureau
- Natural Science Foundation of Zhejiang Province