Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy
The integration of deep learning with MC simulations significantly enhances the efficiency of surface dose calculations in superficial electron beam radiotherapy. The C3D model provides rapid and accurate dose predictions, facilitating efficient treatment planning while maintaining high accuracy.
Authors
- Yuanjie Bi (ORCID: https://orcid.org/0009-0005-8202-1466)
- Yongsheng Huang (ORCID: https://orcid.org/0000-0002-6114-7730)
- Zhitao Dai (ORCID: https://orcid.org/0000-0003-0554-689X)
- Yuanchun Ye (ORCID: https://orcid.org/0000-0002-1514-2605)
- Jialin Huang
- Tianye Niu (ORCID: https://orcid.org/0000-0003-4181-3641)
- Shuai Hu (ORCID: https://orcid.org/0000-0002-2017-381X)
- Yuling Chen (ORCID: https://orcid.org/0000-0002-0591-7964)
- Ming Li (ORCID: https://orcid.org/0000-0003-3081-2424)
- Jinfen Zheng
Institutions
- Guangdong University of Technology (CN)
- Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- The Seventh Affiliated Hospital of Sun Yat-sen University (CN)
- Shenzhen Bay Laboratory (CN)
- Shenzhen Chronic Disease Prevention Center (CN)
- Institute of High Energy Physics (CN)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- Precision Radiation Oncology
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1002/pro6.70015
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88