On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy.

Autor: Sevillano D; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Capuz AB; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Gómez A; Department of Medical Physics, Genesiscare, Madrid, Spain., Colmenares R; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Morís R; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., García JD; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Alonso M; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Cámara M; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Martínez AM; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Béjar MJ; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Prieto D; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain., Sancho S; Department of Radiation Oncology, Hospital Universitario Ramón y Cajal, Madrid, Spain., Chevalier M; Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain., García-Vicente F; Department of Medical Physics, Hospital Universitario Ramón y Cajal, Madrid, Spain.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2019 Oct; Vol. 46 (10), pp. 4622-4630. Date of Electronic Publication: 2019 Aug 20.
DOI: 10.1002/mp.13745
Abstrakt: Purpose: To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery.
Materials and Methods: The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients.
Results: Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted.
Conclusions: The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.
(© 2019 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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