Abstract
Purpose: We investigated the movement characteristics of lung cancers and the clinical accuracy of tracking lung tumors with Synchrony Respiratory Tracking System (SRTs) during the CyberKnife treatment. We also explored the influencing factors of accuracy. These data provided the appropriate expansion margins of patients with different respiratory characteristics, which was helpful to realize the personalized design of treatment plans of CyberKnife. Methods and Materials: 73 patients with lung cancer treated with CyberKnife SRTs were selected retrospectively for this study. The patient's age, gender, respiratory characteristics and tumor datas (tumor size, anatomical position and geometric position) were recorded. During treatment, the deviation was checked every 45 s and compensated by the synchronous respiratory tracking system.Results: The total mean motion amplitudes and standard deviations of lung tumors in superior-inferior (SI), left-right (LR), and anterior-posterior (AP) directions were 4.15 ± 3.47 mm, 3.98 ± 3.21 mm and 3.79 ± 2.73 mm, respectively. The overall mean correlation errors and standard deviations were 0.86 ± 0.45 mm, 1.04 ± 0.76 mm and 0.70 ± 0.47 mm, respectively. The overall mean prediction errors and standard deviations were 0.18 ± 0.17 mm, 0.35 ± 0.39 mm and 0.35 ± 0.42 mm, respectively. The correlation errors of LR direction were less correlated with the geometric position of the tumor (r = 0.38), and not correlated with the anatomical position of the tumor (r < 0.3). The prediction errors were moderately correlated with the respiratory amplitude (r = 0.588), and less correlated with the baseline drift and the motion amplitude of the tumor (r = 0.407 and 0.365, respectively).Conclusions: The patient’s respiratory amplitude, the tumor motion amplitude, the tumor baseline drift and geometric position were the main factors affecting the tracking accuracy. Tumors at different geometric positions should be treated differently to ensure sufficient dose coverage of the lung tumor target.
Design and validation of a
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imaging‐based markerless tracking system for assessing real‐time lung tumor motion