Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input.

Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input. Phys Med Biol. 2019 Oct 01;: Authors: Jiang K, Fujii F, Shiinoki T Abstract The present note addresses the development of a lung tumor position predictor to be used in dynamic tumor tracking radiotherapy, abbreviated as DTT-RT. As there exists 50-500 ms positioning lag in the control of the multi-leaf collimator (MLC) of commercial medical linear accelerators, prediction of future lung tumor position with sufficiently long prediction horizon is inevitable for the successful implementation of DTT-RT. The present article proposes a lung tumor position predictor, which is classified as a nonlinear autoregressive model with exogenous input (NARX). The proposed predictor was trained using seven lung tumor motion trajectories of patients who underwent respiratory-gated radiotherapy at Yamaguchi University Hospital. We considered three different prediction horizons, 600 ms, 800 ms and 1 s, which were sufficiently long to compensate for the possible positioning control lag of the MLC. A patient-specific model corresponding to an intended prediction horizon was obtained by training it using the selected tumor motion trajectory with the specified horizon. Accordingly, we obtained three NARX predictors for a single patient. We calculated two performance metrics: the RMS prediction errors and the rate of coverage of the entire tumor trajectory defined by t...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research