Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.
Journal:
Annals of the New York Academy of Sciences
PMID:
27627049
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.
Authors
Keywords
Bayes Theorem
Computational Biology
Databases, Factual
Empirical Research
Forecasting
Humans
In Vivo Dosimetry
Kinetics
Longitudinal Studies
Machine Learning
Models, Biological
Neoplasms
Neural Networks, Computer
Quality Assurance, Health Care
Radiosurgery
Regression Analysis
Reproducibility of Results