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arXiv · 1309.4144

A location-mixture autoregressive model for online forecasting of lung tumor motion

Daniel Cervone·Natesh S. Pillai·Debdeep Pati·Ross Berbeco·John Henry Lewis
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Abstract

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.

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Daniel Cervone, Natesh S. Pillai, Debdeep Pati, Ross Berbeco, John Henry Lewis. 2014-11-05. A location-mixture autoregressive model for online forecasting of lung tumor motion. https://doi.org/10.1214/14-aoas744

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