Objective:Precision radiation therapy requires managing motions of organs at risk that occur during treatment. While methods have been developed for real-time respiratory motion tracking, non-breathing intra-fractional variations (including gastric contractile motion) have seen little attention to date. The purpose of this study is to develop a cyclic gastric contractile motion prediction model to support real-time management during radiotherapy.Approach:The observed short-term reproducibility of gastric contractile motion permitted development of a prediction model that 1) extracts gastric contraction motion phases from few minutes of golden angle stack of stars scanning (at patient positioning), 2) estimate gastric phase of real-time sampled data acquired during treatment delivery to these reconstructed phases and 3) predicting future gastric phase by linear extrapolation using estimation results from step 2 to account for processing and system latency times. Model was evaluated on three parameters including training time window for step 1, number of spokes for real-time sampling data in step 2 and future prediction time.Main Results:The model was tested on a population of 20-minute data samples from 25 scans from 15 patients. The mean prediction error with 10 spokes and 2 minutes training was 0.3±0.1 mm (0.1 – 0.7 mm) with 5.1 s future time, slowly rising to 0.6±0.2 mm (0.2 – 1.1 mm) for 6.8 s future time and then increasing rapidly for longer forward predictions, for an average 3.6±0.5 mm (2.8 – 4.7 mm) HD95 of gastric motion. Results showed that reducing of train time window (5 to 2 min) doesn’t influence the prediction performance, while using 5 spokes increased prediction errors.Significance:The proposed gastric motion prediction model has sufficiently accurate prediction performance to allow for sub-millimeter accuracy while allowing sufficient time for data processing and machine interaction and shows the potential for clinical implementation to support stomach motion tracking during radiotherapy.