A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks
This study proposes a dose mask-assisted method for lung cancer IMRT dose prediction. It demonstrates high accuracy and robustness in clinical radiotherapy scenarios with various prescription schemes, including conventional radiotherapy and SIB. The inclusion of additional dose masks significantly improved model performance, with prediction accuracy increasing as the number of masks increased.
Authors
- Xinyan Lin (ORCID: https://orcid.org/0000-0002-8878-5396)
- Guoping Zuo (ORCID: https://orcid.org/0000-0002-4227-016X)
- Can Li (ORCID: https://orcid.org/0000-0003-3795-2008)
- Xuezhen Feng
- Ruijie Yang (ORCID: https://orcid.org/0000-0002-2836-8005)
- Yuxi Pan
- Mingqing Wang (ORCID: https://orcid.org/0009-0003-9988-1337)
Institutions
- Peking University (CN)
- Beijing University of Technology (CN)
- Peking University Cancer Hospital (CN)
- Peking University Third Hospital (CN)
- Beijing Information Science & Technology University (CN)
- Beihang University (CN)
- University of South China (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-08-19
- DOI
- https://doi.org/10.3389/fonc.2025.1587788
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China