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September 2014 A location-mixture autoregressive model for online forecasting of lung tumor motion
Daniel Cervone, Natesh S. Pillai, Debdeep Pati, Ross Berbeco, John Henry Lewis
Ann. Appl. Stat. 8(3): 1341-1371 (September 2014). DOI: 10.1214/14-AOAS744

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. "A location-mixture autoregressive model for online forecasting of lung tumor motion." Ann. Appl. Stat. 8 (3) 1341 - 1371, September 2014. https://doi.org/10.1214/14-AOAS744

Information

Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1303.62058
MathSciNet: MR3271335
Digital Object Identifier: 10.1214/14-AOAS744

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 3 • September 2014
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