A single reference measurement can predict liver tumor motion during respiration

Aim: To evaluate liver tumor motion and how well reference measurement predicts motion during treatment. Material and methods: This retrospective study included 20 patients with colorectal cancer that had metastasized to the liver who were treated with stereotactic ablative radiotherapy. An online respiratory tumor tracking system was used. Tumor motion amplitudes in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions were collected to generate patient-specific margins. Reference margins were generated as the mean motion and 95th percentile of motion from measurements recorded for different lengths of time (1, 3, and 5 min). We analyzed the predictability of tumor motion in each axis, based on the reference measurement and intra-/interfraction motions. Results: About 96,000 amplitudes were analyzed. The mean tumor motions were 9.9 +/- 4.2 mm, 2.6 +/- 0.8 mm, and 4.5 +/- 1.8 mm in the SI, LL, and AP directions, respectively. The intrafraction variations were 3.5 +/- 1.8 mm, 0.63 +/- 0.35 mm, and 1.4 +/- 0.65 mm for the SI, LL, and AP directions, respectively. The interfraction motion variations were 1.32 +/- 0.79 mm, 0.31 +/- 0.23 mm, and 0.68 +/- 0.62 mm for the SI, LL, and AP directions, respectively. The Pear son's correlation coefficients for margins based on the reference measurement (mean motion or 95th percentile) and margins covering 95% of the motion during the whole treatment were 0.8-0.91, 0.57-0.7, and 0.77-0.82 in the SI, LL, and AP directions, respectively. Conclusion: Liver tumor motion in the SI direction can be adequately represented by the mean tumor motion amplitude generated from a single 1 min reference measurement. Longer reference measurements did not improve results for patients who were well-educated about the importance of regular breathing. Although the study was based on tumor tracking data, the results are useful for ITV delineation when tumor tracking is not available.Web of Science21328327