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Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

ABSTRACT: Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

SUBMITTER: Li Q

PROVIDER: S-EPMC5026311 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Li Qiongge Q Chan Maria F MF

Annals of the New York Academy of Sciences 20160914 1

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, a ...[more]

PMID: 27627049

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