Radiotherapy dose prediction using off‐the‐shelf segmentation networks: A feasibility study with GammaPod planning
Our study confirms the feasibility of leveraging the segmentation network for dose prediction and provides an efficient and off-the-shelf approach for dose prediction without requiring extensive coding efforts. This plug-in tool holds promise for quick dose planning, potentially aiding in the identification of optimal radiotherapy techniques and dosimetric tradeoffs prior to tedious treatment planning.
Authors
- Xuejun Gu (ORCID: https://orcid.org/0000-0001-6725-6906)
- Weiguo Lu (ORCID: https://orcid.org/0000-0003-3036-7287)
- Mingli Chen (ORCID: https://orcid.org/0000-0002-7005-3816)
- Zi Yang (ORCID: https://orcid.org/0000-0003-3741-5720)
- Kangning Zhang
- Qingying Wang
- Mahdieh Kazemimoghadam (ORCID: https://orcid.org/0000-0002-2609-5517)
Institutions
- The University of Texas Southwestern Medical Center (US)
- Stanford University (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1002/mp.17711
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.77
Funders
- National Institutes of Health