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.
@article{arxiv.1309.4144,
title = {A location-mixture autoregressive model for online forecasting of lung tumor motion},
author = {Daniel Cervone and Natesh S. Pillai and Debdeep Pati and Ross Berbeco and John Henry Lewis},
journal= {arXiv preprint arXiv:1309.4144},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/14-AOAS744 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)