Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer
ML automated RT treatment plan adaptation increases robustness to interfractional anatomical changes compared to current clinical adaptive RT practices by increasing compliance to treatment objectives.
Authors
- Aly Khalifa (ORCID: https://orcid.org/0000-0002-7084-1345)
- Thomas G. Purdie (ORCID: https://orcid.org/0000-0003-4176-8457)
- Jeff D. Winter (ORCID: https://orcid.org/0000-0003-0873-0254)
- Chris McIntosh (ORCID: https://orcid.org/0000-0003-1371-1250)
- Tony Tadic
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- Toronto General Hospital (CA)
- Princess Margaret Cancer Centre (CA)
- Vector Institute (CA)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2024-09-14
- DOI
- https://doi.org/10.1016/j.phro.2024.100649
- Citations
- 3
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- Princess Margaret Cancer Foundation
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada