Comparison of dynamic tumor tracking error measurement methods for robotic radiosurgery

• Published in: Journal of applied clinical medical physics Vol. 24; no. 8; pp. e14093 - n/a
• Main Author: Okawa, Kohei
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.08.2023; John Wiley and Sons Inc
• Subjects: CyberKnife; dynamic tumor tracking; Liver cancer; Machine learning; Magnetic resonance imaging; Methods; Radiation Oncology Physics; Radiation therapy; Radiosurgery; Regression analysis; Respiration; Robotics; tracking error; Velocity; United States > US; tracking error; dynamic tumor tracking; CyberKnife
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.14093

Background Dynamic tumor motion tracking is used in robotic radiosurgery for targets subject to respiratory motion, such as lung and liver cancers. Different methods of measuring tracking error have been reported, but the differences among these methods have not been studied, and the optimal method is unknown. Purpose The purpose of this study was to assess and compare tracking errors encountered with individual patients using different evaluation methods for method optimization. Methods We compared the beam's eye view (BEV), machine learning (ML), log (addition error: AE), and log (root sum square: RSS) methods. Log (AE) and log (RSS) were calculated from log files. These tracking errors were compared, and the optimal evaluation method was ascertained. A t‐test was performed to evaluate statistically significant differences. Here, the significance level was set at 5%. Results The mean values of BEV, log (AE), log (RSS), and ML were 2.87, 3.91, 2.91, and 3.74 mm, respectively. The log (AE) and ML were higher than BEV (p < 0.001), and log (RSS) was equivalent to the BEV, suggesting that the log (RSS) calculated with the log file method can substitute for the BEV calculated with the BEV method. As RSS error calculation is simpler than BEV calculation, using it may improve clinical practice throughput. Conclusion This study clarified differences among three tracking error evaluation methods for dynamic tumor tracking radiotherapy using a robotic radiosurgery system. The log (RSS) calculated by the log file method was found to be the best alternative to BEV method, as it can calculate tracking errors more easily than the BEV method.