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

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.

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)

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• Optimal Nonlinear Prediction of Random Fields on Networks
• LICORS: Light Cone Reconstruction of States for Non-parametric Forecasting of Spatio-Temporal Systems
• Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
• A location-mixture autoregressive model for online forecasting of lung tumor motion

Cited by in corpus (2)

• A location-mixture autoregressive model for online forecasting of lung tumor motion
• On Construction and Estimation of Stationary Mixture Transition Distribution Models