A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer
Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.
Authors
- Susan Harden (ORCID: https://orcid.org/0000-0001-6067-7177)
- Sandro Porceddu (ORCID: https://orcid.org/0000-0003-0937-6939)
- Katrina Woodford (ORCID: https://orcid.org/0009-0009-9411-153X)
- Nicholas Hardcastle (ORCID: https://orcid.org/0000-0001-7796-8472)
- Lachlan McIntosh (ORCID: https://orcid.org/0009-0005-3543-0259)
- Vanessa Panettieri (ORCID: https://orcid.org/0000-0002-4061-1960)
- Mathieu Gaudreault
- Jason Li (ORCID: https://orcid.org/0000-0002-1150-3549)
Institutions
- The University of Melbourne (AU)
- University of Wollongong (AU)
- Peter MacCallum Cancer Centre (AU)
- Monash University (AU)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1016/j.phro.2025.100890
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Computational Infrastructure
- University of Wollongong
- Peter MacCallum Cancer Centre
- Cancer Council Victoria