Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions
This study developed a fully automated IMRT plan design method for rectal cancer. This approach significantly improved the efficiency of designing IMRT plans and plan quality.
Authors
- Meining Chen
- Zhiwei Leng (ORCID: https://orcid.org/0000-0002-1335-4006)
- R.C. Chen
- Qichao Zhou
- Lin Huang (ORCID: https://orcid.org/0000-0003-3264-1374)
- Xiaowu Deng (ORCID: https://orcid.org/0000-0003-2923-1251)
- Zhaocai Chen
- Yaoying Liu (ORCID: https://orcid.org/0000-0002-6282-9936)
- Jun Zhang (ORCID: https://orcid.org/0000-0001-7835-9871)
- Yimei Liu (ORCID: https://orcid.org/0000-0003-3344-4938)
- Yinglin Peng (ORCID: https://orcid.org/0000-0003-3977-9042)
- Zhenyu Qi (ORCID: https://orcid.org/0009-0003-1195-3771)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-11-11
- DOI
- https://doi.org/10.21037/qims-2025-168
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88