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

• 成果类型: Article
• 署名作者: Cervone, Daniel; Pillai, Natesh S.; Pati, Debdeep; Berbeco, Ross; Lewis, John Henry
• 署名单位: Harvard University; State University System of Florida; Florida State University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Brigham & Women's Hospital; Harvard University; Harvard Medical School
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/14-AOAS744
• 发表日期: 2014
• 页码: 1341-1371
• 关键词: time-series; respiratory motion; comparative performance; prediction; likelihood; networks; tracking

摘要

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.