Lung cancer dose distribution prediction based on a dual‐branch feature extraction network
The proposed CTNet uses the transformer and convolutional networks to extract global information, such as the relative position of the PTV and OARs, as well as local information, such as shape and size, enabling accurate prediction of the dose distribution for NSCLC patients undergoing VMAT radiotherapy.
Authors
- Fuli Zhang (ORCID: https://orcid.org/0000-0002-4175-4795)
- Haifeng Zhang (ORCID: https://orcid.org/0000-0003-4915-0328)
- Yanjun Yu
- Yong-Xin Liu (ORCID: https://orcid.org/0000-0002-6506-7148)
Institutions
- Chinese PLA General Hospital (CN)
- Shandong First Medical University (CN)
- Beihang University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1002/mp.17775
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00