The Annals of Applied Statistics

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

Daniel Cervone, Natesh S. Pillai, Debdeep Pati, Ross Berbeco, and John Henry Lewis

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1341-1371.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091216

Digital Object Identifier
doi:10.1214/14-AOAS744

Mathematical Reviews number (MathSciNet)
MR3271335

Zentralblatt MATH identifier
1303.62058

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

Citation

Cervone, Daniel; Pillai, Natesh S.; Pati, Debdeep; Berbeco, Ross; Lewis, John Henry. A location-mixture autoregressive model for online forecasting of lung tumor motion. Ann. Appl. Stat. 8 (2014), no. 3, 1341--1371. doi:10.1214/14-AOAS744. https://projecteuclid.org/euclid.aoas/1414091216


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