A location-mixture autoregressive model for online forecasting of lung tumor motion. (English) Zbl 1303.62058
Summary: Lung tumor tracking for radiotherapy requires real-time, multiple-step ahead forecasting of a quasi-periodic time series recording instantaneous tumor locations. We introduce a location-mixture autoregressive (LMAR) process that admits multimodal conditional distributions, fast approximate inference using the EM algorithm and accurate multiple-step ahead predictive distributions. LMAR outperforms several commonly used methods in terms of out-of-sample prediction accuracy using clinical data from lung tumor patients. With its superior predictive performance and real-time computation, the LMAR model could be effectively implemented for use in current tumor tracking systems.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 62M20 | Inference from stochastic processes and prediction |
| 62M05 | Markov processes: estimation; hidden Markov models |
| 92C50 | Medical applications (general) |
Keywords:
lung tumor tracking; external beam radiotheraphy; nonlinear time series; mixture autoregressive process; time series motifs; likelihood approximation; multiple-step predictionReferences:
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