Closing the gap in plan quality: Leveraging deep‐learning dose prediction for adaptive radiotherapy
Our preliminary results demonstrate the feasibility of utilizing an AI dose predictor to predict optimal planning goals with anatomical changes, thereby improving adaptive plan quality. This method is feasible for both online and offline adaptive radiotherapy (ART) and has the potential to significantly enhance treatment outcomes for head-and-neck (H&N) cancer patients.
Authors
- Michael Dohopolski (ORCID: https://orcid.org/0000-0002-9043-1490)
- Dominic H. Moon (ORCID: https://orcid.org/0000-0001-9161-7783)
- David J. Sher (ORCID: https://orcid.org/0000-0003-3943-7480)
- Steve Jiang (ORCID: https://orcid.org/0000-0002-3083-6752)
- Sean Domal (ORCID: https://orcid.org/0000-0002-5988-5240)
- Justin Visak
- M.H. Lin
- Dan Nguyen (ORCID: https://orcid.org/0000-0002-9590-0655)
- Austen Maniscalco (ORCID: https://orcid.org/0000-0001-7156-3239)
- Vladimir Avkshtol
Institutions
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1002/acm2.70045
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00