A LOCATION-MIXTURE AUTOREGRESSIVE MODEL FOR ONLINE FORECASTING OF LUNG TUMOR MOTION

作者：Cervone Daniel^*; Pillai Natesh S; Pati Debdeep; Berbeco Ross; Lewis John Henry

来源：Annals of Applied Statistics, 2014, 8(3): 1341-1371.

DOI：10.1214/14-AOAS744

• Lung tumor tracking
• external beam radiotherapy
• nonlinear time series
• mixture autoregressive process
• time series motifs
• likelihood approximation
• multiple-step prediction

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.

• 出版日期2014-9