Prediction of portal dosimetry quality assurance results using log files-derived errors and machine learning techniques

OBJECTIVE: This work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime. METHODS: 578...

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Detalles Bibliográficos
Autores principales: Lew, Kah Seng, Chua, Clifford Ghee Ann, Koh, Calvin Wei Yang, Lee, James Cheow Lei, Park, Sung Yong, Tan, Hong Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880542/
https://www.ncbi.nlm.nih.gov/pubmed/36713533
http://dx.doi.org/10.3389/fonc.2022.1096838

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880542/
https://www.ncbi.nlm.nih.gov/pubmed/36713533
http://dx.doi.org/10.3389/fonc.2022.1096838

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