Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data
Applying transfer learning to dose prediction models significantly improves prediction accuracy for PTV while maintaining similar dose performance in predicting organ-at-risk (OAR) dose compared to pretrained and baseline models. This approach enhances dose prediction models for novel radiotherapy methods with limited training data.
Authors
- David J. Sher (ORCID: https://orcid.org/0000-0003-3943-7480)
- Mu‐Han Lin (ORCID: https://orcid.org/0000-0002-7992-5465)
- Steve Jiang (ORCID: https://orcid.org/0000-0002-3083-6752)
- Dan Nguyen (ORCID: https://orcid.org/0000-0002-9590-0655)
- Austen Maniscalco (ORCID: https://orcid.org/0000-0001-7156-3239)
- Hui‐Ju Wang (ORCID: https://orcid.org/0000-0002-3816-8664)
Institutions
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1002/acm2.70012
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health