Enhancing patient‐specific quality assurance for VMAT for breast cancer treatment: A machine learning approach for gamma passing rate (GPR) prediction
This study highlights the promise of ML-based approaches in streamlining PSQA processes, thereby supporting the quality assurance of breast cancer treatments using VMAT.
Authors
- N.A.G. Moyo (ORCID: https://orcid.org/0000-0002-2883-0677)
- Christopher F. Njeh (ORCID: https://orcid.org/0000-0002-9433-3548)
- Anthony Colliaux
- Valérie Blot‐Lafond
- Francis C. Djoumessi Zamo (ORCID: https://orcid.org/0000-0003-3385-8276)
Institutions
- University of Douala (CM)
- Centre de Radiothérapie Bayard (FR)
- Centre Hospitalier d'Angoulême (FR)
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/acm2.70251
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Abdus Salam International Centre for Theoretical Physics