Target margin design through analyzing a large cohort of clinical log data in the cyberknife system

Abstract

Purpose Calculating the adequate target margin for real‐time tumor tracking using the Cyberknife system is a challenging issue since different sources of error exist. In this study, the clinical log data of the Cyberknife system were analyzed to adequately quantify the planned target volume (PTV) margins of tumors located in the lung and abdomen regions. Methods In this study, 45 patients treated with the Cyberknife module were examined. In this context, adequate PTV margins were estimated based on the Van Herk formulation and the uncertainty estimation method by considering the impact of errors and uncertainties. To investigate the impact of errors and uncertainties on the estimated PTV margins, a statistical analysis was also performed. Results Our study demonstrates five different sources of errors, including segmentation, deformation, correlation, prediction, and targeting errors, which were identified as the main sources of error in the Cyberknife system. Furthermore, the clinical evaluation of the current study reveals that the two different formalisms provided almost identical PTV margin estimates. Additionally, 4–5 mm and 5 mm margins on average could provide adequate PTV margins at lung and abdomen tumors in all three directions, respectively. Overall, it was found that concerning the PTV margins, the impact of correlation and prediction errors is very high, while the impact of robotics error is low. Conclusions The current study can address two limitations in previous researches, namely insufficient sample sites and a smaller number of patients. A comparison of the present results concerning the lung and abdomen areas with other studies reveals that the proposed strategy could provide a better reference in selection the PTV margins. To our knowledge, this study is one of the first attempts to estimate the PTV margins in the lung and abdomen regions for a large cohort of patients treated using the Cyberknife system.

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study

“…The fundamental concept of the SRTS is synchronizing the respiratory with the tumor position. Although the breathing signal is monitored every 10–40 ms through the optical camera system, the target and marker positions are determined per X‐ray image acquisition 3,8 . Therefore, a correlation model can track the tumor movement using the correlation between the patient's respiration and tumor position.…”

“…The correlation model is also updated every 1–6 min throughout treatment using periodic X‐ray images. Note that the 115 ms system latency, due to robotic response or data acquisition, affects the accuracy of radiation dose delivery 3 . Therefore, the Synchrony system employs a predictive model to correct this effect.…”

“…Although the breathing signal is monitored every 10-40 ms through the optical camera system, the target and marker positions are determined per X-ray image acquisition. 3,8 Therefore, a correlation model can track the tumor movement using the correlation between the patient's respiration and tumor position. The correlation model is also updated every 1-6 min throughout treatment using periodic X-ray images.…”

“…Overall, the goal of respiratory gating or real‐time tumor tracking methods is to detect, predict, and compensate for respiratory motion during the planning and delivery of radiotherapy. However, the inherent delay present in modern radiotherapy equipment caused by data processing, saving, reading, radiation field adjustment, and communication with the robot controller affects the accuracy of radiation dose delivery 3,4 . In this regard, a compensation strategy is required in modern radiotherapy devices to minimize the effects of system latency.…”

The impact of the ClearRT™ upgrade on target motion tracking accuracy in Radixact® Synchrony® lung treatments

“…Deformation error is determined by the influence of internal organ motion on the target position or change in target size or shape during the treatment course. Correlation, prediction and target errors are mainly determined by the system performance [ 9 , 13 ].…”