Breast radiotherapy planning: A decision‐making framework using deep learning
The proposed decision-making model accurately predicts dose distributions for both 3D-CRT and IMRT, ensuring reliable OAR dose estimation. This decision-making framework significantly outperforms historical decision-making, demonstrating higher accuracy, recall, and precision.
Authors
- Oliver Díaz (ORCID: https://orcid.org/0000-0001-6789-5177)
- Pedro Gallego (ORCID: https://orcid.org/0000-0002-3658-2431)
- J. Pérez-Alija
- Simone Balocco (ORCID: https://orcid.org/0000-0002-7149-7806)
- J.M. Roda
- Chris M. Anson (ORCID: https://orcid.org/0000-0002-9799-3111)
- Helena Vivancos
- Marta Barceló
- N. Jornet (ORCID: https://orcid.org/0000-0003-0476-1344)
- P. Carrasco (ORCID: https://orcid.org/0000-0002-7893-5813)
- Alejandro Domínguez (ORCID: https://orcid.org/0000-0003-3990-2290)
- Victor Riu
- E. Ambroa
- Natalia Tejedor
- A. Castillo Ruiz
Institutions
- Hospital de Sant Pau (ES)
- Consorci Sanitari de Terrassa (ES)
- Centre de Recerca Matemàtica (ES)
- Computer Vision Center
- Universitat de Barcelona (ES)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1002/mp.17527
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.22
Funders
- Ministerio de Ciencia e Innovación