Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques
We demonstrated that our deep learning model can consistently predict high quality VMAT lung plans for a variety of prescription doses. The dose prediction tool was also effective in identifying suboptimal plan quality, suggesting its potential benefit in automated treatment planning and evaluation.
Authors
- Wenhua Cao (ORCID: https://orcid.org/0000-0002-7795-2929)
- Stephen Bilton
- Hana Baroudi (ORCID: https://orcid.org/0000-0001-9921-4663)
- Thomas J. Whitaker (ORCID: https://orcid.org/0000-0001-8950-9097)
- Laurence E. Court (ORCID: https://orcid.org/0000-0002-3241-6145)
- C Peeler (ORCID: https://orcid.org/0000-0003-3418-076X)
- Skylar Gay (ORCID: https://orcid.org/0000-0003-4659-0766)
- Zhongxing Liao (ORCID: https://orcid.org/0000-0002-2224-9478)
- Karen Elizabeth Hoffman (ORCID: https://orcid.org/0000-0002-7316-8790)
- Mary Gronberg (ORCID: https://orcid.org/0000-0002-0121-9579)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1002/mp.17692
- Citations
- 3
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.65
Funders
- Cancer Prevention and Research Institute of Texas
- Varian Medical Systems