Organ‐at‐risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT

OBJECTIVE: To quantify the clinical performance of a machine learning (ML) algorithm for organ‐at‐risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions. METHODS: ML models were t...

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Detalles Bibliográficos
Autores principales: Brodin, N. Patrik, Schulte, Leslie, Velten, Christian, Martin, William, Shen, Sydney, Shen, Jin, Basavatia, Amar, Ohri, Nitin, Garg, Madhur K., Carpenter, Colin, Tomé, Wolfgang A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Radiation Oncology Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195027/
https://www.ncbi.nlm.nih.gov/pubmed/35460150
http://dx.doi.org/10.1002/acm2.13609

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195027/
https://www.ncbi.nlm.nih.gov/pubmed/35460150
http://dx.doi.org/10.1002/acm2.13609

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