A TPS integrated machine learning tool for predicting patient-specific quality assurance outcomes in volumetric-modulated arc therapy
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Published:2024-02
Issue:
Volume:118
Page:103208
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ISSN:1120-1797
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Container-title:Physica Medica
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language:en
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Short-container-title:Physica Medica
Author:
Noblet CarolineORCID,
Maunet MathisORCID,
Duthy Marie,
Coste Frédéric,
Moreau Matthieu
Reference45 articles.
1. Chiavassa S, Bessieres I, Edouard M, Mathot M, Moignier A. Complexity metrics for IMRT and VMAT plans: a review of current literature and applications. Br J Radiol. 2019. 92. https://doi.org/https://doi.org/10.1259/bjr.20190270.
2. Use of metrics to quantify IMRT and VMAT treatment plan complexity: A systematic review and perspectives;Antoine;Phys Med,2019
3. Miften M, Olch A, Mihailidis D, Moran J, Pawlicki T, Molineu A, et al. Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218. Med Phys. 2018. 45. e53–83. https://doi.org/10.1002/mp.12810.
4. Integration of AI and Machine Learning in Radiotherapy QA. Front;Chan;Artif Intell,2020
5. Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance;Osman;J Appl Clin Med Phys,2021
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