Evaluation of tracking accuracy of the CyberKnife system using a webcam and printed calibrated grid

Novel utilization and quantification of Xsight diaphragm tracking for respiratory motion compensation in Cyberknife Synchrony treatment of liver tumors

PURPOSE

The Xsight lung tracking system (XLTS) utilizes an advanced image processing algorithm to precisely identify the position of a tumor and determine its location in orthogonal x-ray images, instead of finding fiducials, thereby minimizing the risk of fiducial insertion-related side effects. To assess and gauge the effectiveness of CyberKnife Synchrony in treating liver tumors located in close proximity to or within the diaphragm, we employed the Xsight diaphragm tracking system (XDTS), which was based on the XLTS.

METHODS

We looked back at the treatment logs of 11 patients (8/11 [XDTS], 3/11 [Fiducial-based Target Tracking System-FTTS]) who had liver tumors in close proximity to or within the diaphragm. And the results are compared with the patients who undergo the treatment of FTTS. The breathing data information was calculated as a rolling average to reduce the effect of irregular breathing. We tested the tracking accuracy with a dynamic phantom (18023-A) on the basis of patient-specific respiratory curve.

RESULTS

The average values for the XDTS and FTTS correlation errors were 1.38 ± 0.65  versus 1.50 ± 0.26 mm (superior-inferior), 1.28 ± 0.48  versus 0.40 ± 0.09 mm (left-right), and 0.96 ± 0.32  versus 0.47 ± 0.10 mm(anterior-posterior), respectively. The prediction errors for two methods of 0.65 ± 0.16  versus 5.48 ± 3.33 mm in the S-I direction, 0.34 ± 0.10  versus 1.41 ± 0.76 mm in the A-P direction, and 0.22 ± 0.072  versus 1.22 ± 0.48 mm in the L-R direction. The coverage rate of FTTS slightly less than that of XDTS, such as 96.53 ± 8.19% (FTTS) versus 98.03 ± 1.54 (XDTS). The prediction error, the motion amplitude, and the variation of the respiratory center phase were strongly related to each other. Especially, the higher the amplitude and the variation, the higher the prediction error.

CONCLUSION

The diaphragm has the potential to serve as an alternative to gold fiducial markers for detecting liver tumors in close proximity or within it. We also found that we needed to reduce the motion amplitude and train the respiration of the patients during liver radiotherapy, as well as control and evaluate their breathing.

Respiratory motion tracking of spine stereotactic radiotherapy in prone position

PURPOSE

The CyberKnife system is a specialized device for non-coplanar irradiation; however, it possesses the geometric restriction that the beam cannot be irradiated from under the treatment couch. Prone positioning is expected to reduce the dose to normal lung tissue in spinal stereotactic body radiotherapy (SBRT) owing to the efficiency of beam arrangement; however, respiratory motion occurs. Therefore, the Xsight spine prone tracking (XSPT) system is used to reduce the effects of respiratory motion. The purpose of this study was to evaluate the motion-tracking error of the spine in the prone position.

MATERIALS AND METHODS

Data from all 25 patients who underwent spinal SBRT at our institution between April 2020 and February 2022 using CyberKnife (VSI, version 11.1.0) with the XSPT tracking system were retrospectively analyzed using log files. The tumor motion, correlation, and prediction errors for each patient were examined. Furthermore, to assess the potential relationships between the parameters, the relationships between the tumor-motion amplitudes and correlation or prediction errors were investigated using linear regression.

RESULTS

The tumor-motion amplitudes in each direction were as follows: superior-inferior (SI), 0.51 ± 0.39 mm; left-right (LR), 0.37 ± 0.29 mm; and anterior-posterior (AP), 3.43 ± 1.63 mm. The overall mean correlation and prediction errors were 0.66 ± 0.48 mm and 0.06 ± 0.07 mm, respectively. The prediction errors were strongly correlated with the tumor-motion amplitudes, whereas the correlation errors were not.

CONCLUSIONS

This study demonstrated that the correlation error of spinal SBRT in the prone position is sufficiently small to be independent of the tumor-motion amplitude. Furthermore, the prediction error is small, contributing only slightly to the tracking error. These findings will improve the understanding of how to compensate for respiratory-motion uncertainty in the prone position.

Dosimetric comparison of Synchrony® real-time motion tracking treatment plans between CyberKnife robotic radiosurgery and Radixact system for stereotactic body radiation therapy of lung and prostate cancer

The aim of this study was to assess which machine, Radixact or CyberKnife, can deliver better treatment for lung and prostate stereotactic body radiation therapy (SBRT) with the use of Synchrony® real-time motion tracking system. Ten and eight patients treated with lung and prostate SBRT, respectively, using the CyberKnife system were selected for the assessment. For each patient, a retrospective Radixact plan was created and compared with the original CyberKnife plan. There was no statistically significant difference in the new conformity index of the Radixact plans and that of the Cyberknife plans in both lung and prostate SBRT. The average homogeneity index in the Radixact plans was better in both lung and prostate SBRT with statistical significance (p = 0·04 for lung and p = 0·02 for prostate). In lung SBRT, the dose to lungs was lower in Cyberknife plans (p = 0·002). In prostate SBRT, there was no statistically significant difference in organs at risk sparing between Cyberknife plans and Radixact plans. In conclusion, CyberKnife was better in lung SBRT while Radixact was better in prostate SBRT.

Development of a tracking error prediction system for the CyberKnife Synchrony Respiratory Tracking System with use of support vector regression

PURPOSE

The accuracy of the CyberKnife Synchrony Respiratory Tracking System is dependent on the breathing pattern of a patient. Therefore, the tracking error in each patient must be determined. Support vector regression (SVR) can be used to easily identify the tracking error in each patient. This study aimed to develop a system with SVR that can predict tracking error according to a patient's respiratory waveform.

METHODS

Datasets of the respiratory waveforms of 93 patients were obtained. The feature variables were variation in respiration amplitude, tumor velocity, and phase shift between tumor and the chest wall, and the target variable was tracking error. A learning model was evaluated with tenfold cross-validation. We documented the difference between the predicted and actual tracking errors and assessed the correlation coefficient and coefficient of determination.

RESULTS

The average difference and maximum difference between the actual and predicted tracking errors were 0.57 ± 0.63 mm and 2.1 mm, respectively. The correlation coefficient and coefficient of determination were 0.86 and 0.74, respectively.

CONCLUSION

We developed a system for obtaining tracking error by using SVR. The accuracy of such a system is clinically useful. Moreover, the system can easily evaluate tracking error. We developed a system that can be used to predict the tracking error of SRTS in the CyberKnife Robotic Radiosurgery System using machine learning. The feature variables were the breathing parameters, and the target variable was the tracking error. We used support vector regression algorithm.

Comparison of modeling accuracy between Radixact®and CyberKnife®Synchrony®respiratory tracking system

Synchrony Respiratory Tracking system adapted from CyberKnife has been introduced in Radixact to compensate the tumor motion caused by respiration. This study aims to compare the modeling accuracy of the Synchrony system between Radixact and CyberKnife. Two Synchrony plans based on fiducial phantoms were created for CyberKnife and Radixact, respectively. Different respiratory motion traces were used to drive a motion platform to move along the superoinferior and left-right direction. The cycle time and the amplitude of target/surrogate motion of one selected motion trace were scaled to investigate the dependence of modeling accuracy on the motion characteristic. The predicted target position, the correlation error, potential difference (Radixact only) and standard error (CyberKnife only) were extracted from raw data or log files of the two systems. The modeling accuracy was evaluated by calculating the root-mean-square (RMS) error between the predicted target positions and the input motion trace. A threshold T95 within which 95% of the potential difference or the standard error lay was defined and evaluated. Except for the motion trace with a small amplitude and a good (linear) correlation between target and surrogate motion, Radixact showed smaller RMS errors than CyberKnife. The RMS error of both systems increased with the motion amplitude and showed a decreasing trend with the increasing cycle time. No correlation was found between the RMS error and the amplitude of surrogate motion. T95 could be a good estimator of modeling accuracy for CyberKnife rather than Radixact. The correlation error defined in Radixact were largely affected by the number of fiducial markers and the setup error. In general, the modeling accuracy of the Radixact Synchrony system is better than that of the CyberKnife Synchrony system under unfavorable conditions.

Plan comparison of prostate stereotactic radiotherapy in spacer implant patients

In prostate stereotactic body radiation therapy (SBRT), hydrogel spacers are increasingly used. This study aimed to perform a dosimetry comparison of treatment plans using CyberKnife (CK), commonly used for prostate SBRT, Helical TomoTherapy (HT), and TrueBeam (TB) in patients with hydrogel spacer implantations. The data of 20 patients who received hydrogel spacer implantation for prostate SBRT were retrospectively analyzed. The prescription dose was 36.25 Gy in five fractions to 95% of the planning target volume (PTV; D95). The conformity index (CI), gradient index (GI), homogeneity index (HI), and dose‐volume histogram (DVH) were analyzed for the three modalities, using the same PTV margins. The monitor unit (MU) and the beam‐on‐time (BOT) values were subsequently compared. The CI of TB (0.93 ± 0.02) was significantly superior to those of CK (0.82 ± 0.03, p < 0.01) and HT (0.86 ± 0.03, p < 0.01). Similarly, the GI value of TB (3.59 ± 0.12) was significantly better than those of CK (4.31 ± 0.43, p < 0.01) and HT (4.52 ± 0.24, p < 0.01). The median doses to the bladder did not differ between the CK and TB (V18.1 Gy: 16.5% ± 4.5% vs. 15.8% ± 4.4%, p = 1.00), but were significantly higher for HT (V18.1 Gy: 33.2% ± 7.3%, p < 0.01 vs. CK, p < 0.01 vs. TB). The median rectal dose was significantly lower for TB (V18.1 Gy: 5.6% ± 4.5%) than for CK (V18.1 Gy: 11.2% ± 6.7%, p < 0.01) and HT (20.2% ± 8.3%, p < 0.01). TB had the shortest BOT (2.6 min; CK: 17.4 min, HT: 6.9 min). TB could create treatment plans dosimetrically comparable to those of CK when using the same margins, in patients with hydrogel spacers.

respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking

Purpose

An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments.

Methods

A custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient’s surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient’s 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction.

Results

Two evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.32\pm 0.06~\hbox {mm}$$\end{document}0.32±0.06mm and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.71\pm 0.79~\hbox {mm}$$\end{document}3.71±0.79mm. The overall registration RMS error was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.6\pm 0.4~\hbox {mm}$$\end{document}0.6±0.4mm. The best prediction errors were observed by registrations at half inhaled positions with minimum \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.27\pm 0.02~\hbox {mm}$$\end{document}0.27±0.02mm, maximum \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.90\pm 0.72~\hbox {mm}$$\end{document}2.90±0.72mm. The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine.

Conclusion

The built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients.

Factors affecting the accuracy of respiratory tracking of the image-guided robotic radiosurgery system

PURPOSE

To analyze the factors affecting the tracking accuracy of the CyberKnife Synchrony Respiratory Tracking System (SRTS).

MATERIALS AND METHODS

A dynamic motion phantom (motion phantom) reproduced the respiratory motions of each patient treated with the SRTS using a ball as the target. CyberKnife tracked the ball using the SRTS, and this process was recorded by a video camera mounted on the linear accelerator head. The tracking error was evaluated from the images captured by the video camera. Multiple regression analysis was used to identify factors affecting tracking accuracy from 91 cases.

RESULTS

The median tracking error was 1.9 mm (range 0.9-5.3 mm). Four factors affected the tracking accuracy: the average absolute amplitude of the tumor motion in the cranio-caudal (CC) direction (p = 0.007), average position gap due to the phase shift between the internal tumor and external marker positions in the CC direction (p < 0.001), and average velocity of the tumor in the CC (p < 0.001) and anterior-posterior directions (p = 0.033).

CONCLUSION

We identified factors that affected tracking accuracy. This information may assist the identification of suitable margins that should be added to each patient's clinical target volume.

Impacts of respiratory phase shifts on motion-tracking accuracy of the CyberKnife Synchrony™ Respiratory Tracking System

PURPOSE

The Synchrony^TM Respiratory Tracking System (SRTS) component of the CyberKnife^® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale CA) enables real-time tracking of moving targets by modeling the correlation between the targets and external surrogate light-emitting diode (LED) markers placed on the patient's chest. Previous studies reported some cases with respiratory phase shifts between lung tumor and chest wall motions. In this study, the impacts of respiratory phase shifts on the motion-tracking accuracy of the SRTS were investigated.

METHODS

A plastic scintillator was used to detect the position of the x-ray beams. The scintillation light was recorded using a camera in a dark room. A moving phantom moved a U-shaped frame on the scintillator with a 4th power of sinusoidal functions. Three metallic markers for motion tracking and four fluorescent tapes were attached to the frame. The fluorescent tapes were used to identify phantom position and respiratory phase for each video frame. The beam positions collected, when considered relative to the phantom motion, represent the degree of tracking error. Beam position was calculated by adding error value to phantom position. Motions with respiratory phase shifts between the target and an extra stage mimicking chest wall motion were also tested for LED markers. Log files of the SRTS were analyzed to evaluate correlation errors.

RESULTS

When target and LED marker motions were synchronized with a respiratory cycle of 4 s, the maximum tracking errors for 90% and 95% of beam-on time were 1.0 mm and 1.2 mm, respectively. The frequency of tracking errors increased when LED marker motion phase preceded target motion. Tracking errors that corresponded to 90% beam-on time were within 2.4 mm for 5-15% of phase shifts. In contrast, the tracking errors were very large when the LED marker delayed to the target motions; the maximum errors of 90% beam-on time were 3.0, 3.8, and 7.5 mm for 5%, 10%, and 15% of phase shifts, respectively. The patterns of the tracking errors derived from the scintillation light were very similar to those of the correlation data of the SRTS derived from the log files, indicating that the tracking errors caused mainly due to the errors in modeling the correlation data. With long respiratory cycle of 6 s, the tracking errors were significantly decreased; the maximum tracking errors for 95% beam-on time were 1.6 mm and 2.2 mm for early and delayed LED motion.

CONCLUSION

We have investigated the motion-tracking accuracy of the CyberKnife SRTS for cases with the respiratory phase shift between the target and the LED marker. The maximum tracking errors for 90% probability were within 2.4 mm when the target delays to the LED markers. When LED marker delays, however, very large tracking errors were observed. With a long respiratory cycle, the tracking errors were greatly improved to less than 2.2 mm. Coaching slow breathing will be useful for accurate motion tracking radiotherapy.

Performance evaluation of the CyberKnife system in real-time target tracking during beam delivery using a moving phantom coupled with two-dimensional detector array

The aim of the current study was to evaluate the tracking error of the Synchrony Respiratory Tracking system by conducting beam-by-beam analyses to determine the variation in the tracking beams measured during target motion. A moving phantom of in-house design coupled with a two-dimensional (2D) detector array was used to simulate respiratory motion in the superoinferior (SI) and anteroposterior (AP) direction. A styrofoam block with four implanted fiducial markers was placed on top of the detector to enable the fiducial-based respiratory tracking. Measurements were performed with the phantom under either stationary mode or sinusoidal motion of 6-s cycle and 15/20-mm amplitude at SI and AP direction. The measurement data were saved as movie files that were used to calculate the center shift of the beam with 100-ms sampling time. The tracking accuracy of the system was defined as the targeting error, which could be tracked with probability of > 95% (Ep95). The mean ± standard deviation of Ep95 was 0.28 ± 0.08 mm under stationary condition; 0.66 ± 0.23 mm (range: 0.28-1.22 mm) under sinusoidal respiratory motion. The maximum drift of the beam center for all beam paths was 2.7 mm. The tracking accuracy of CyberKnife Synchrony system was successfully evaluated using a moving phantom and 2D detector array; the maximum tracking error was < 1.5 mm for sinusoidal motion of amplitude ≤ 20 mm.

Evaluation of the 4D RADPOS dosimetry system for dose and position quality assurance of CyberKnife

PURPOSE

The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations.

METHODS

Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation.

RESULTS

For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5 mm for all directions. For the 10 mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2 mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8 mm for sinusoidal motion but increased to ≤2.6 mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system.

CONCLUSIONS

RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

Evaluation of the accuracy of the CyberKnife Synchrony™ Respiratory Tracking System using a plastic scintillator

PURPOSE

The Synchrony™ Respiratory Tracking System of the CyberKnife® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale, CA, USA) enables real-time tracking of moving targets such as lung and liver tumors during radiotherapy. Although film measurements have been used for quality assurance of the tracking system, they cannot evaluate the temporal tracking accuracy. We have developed a verification system using a plastic scintillator that can evaluate the temporal accuracy of the CyberKnife Synchrony.

METHODS

A phantom consisting of a U-shaped plastic frame with three fiducial markers was used. The phantom was moved on a plastic scintillator plate. To identify the phantom position on the recording video in darkness, four pieces of fluorescent tape representing the corners of a 10 cm × 10 cm square around an 8 cm × 8 cm window were attached to the phantom. For a stable respiration model, the phantom was moved with the fourth power of a sinusoidal wave with breathing cycles of 4, 3, and 2 s and an amplitude of 1 cm. To simulate irregular breathing, the respiratory cycle was varied with Gaussian random numbers. A virtual target was generated at the center of the fluorescent markers using the MultiPlan™ treatment planning system. Photon beams were irradiated using a fiducial tracking technique. In a dark room, the fluorescent light of the markers and the scintillation light of the beam position were recorded using a camera. For each video frame, a homography matrix was calculated from the four fluorescent marker positions, and the beam position derived from the scintillation light was corrected. To correct the displacement of the beam position due to oblique irradiation angles and other systematic measurement errors, offset values were derived from measurements with the phantom held stationary.

RESULTS

The average SDs of beam position measured without phantom motion were 0.16 and 0.20 mm for lateral and longitudinal directions, respectively. For the stable respiration model, the tracking errors (mean ± SD) were 0.40 ± 0.64 mm, -0.07 ± 0.79 mm, and 0.45 ± 1.14 mm for breathing cycles of 4, 3, and 2 s, respectively. The tracking errors showed significant linear correlation with the phantom velocity. The correlation coefficients were 0.897, 0.913, and 0.957 for breathing cycles of 4, 3, and 2 s, respectively. The unstable respiration model also showed linear correlation between tracking errors and phantom velocity. The probability of tracking error incidents increased with decreasing length of the respiratory cycles. Although the tracking error incidents increased with larger variations in respiratory cycle, the effect on the cumulative probability was insignificant. For a respiratory cycle of 4 s, the maximum tracking error was 1.10 and 1.43 mm at the probability of 10% and 5%, respectively. Large tracking errors were observed when there was phase shift between the tumor and the LED marker.

CONCLUSION

This technique allows evaluation of the motion tracking accuracy of the Synchrony™ system over time by measurement of the photon beam. The velocity of the target and phase shift have significant effects on accuracy.

Dosimetric impact of intra-fraction prostate motion under a tumour-tracking system in hypofractionated robotic radiosurgery

For CyberKnife-mediated prostate cancer treatment, a tumour-tracking approach is applied to correct the target location by acquiring X-ray images of implanted fiducial markers intermittently. This study investigated the dosimetric impact of intra-fraction prostate motion during CyberKnife treatment. We retrospectively analyzed 16 patients treated using the CyberKnife (35 Gy delivered in five fractions). Using log files of recorded prostate motion, the intra-fraction prostate motion was simulated. We defined the worst-case intra-fraction prostate motion as the difference between pre- and post-deviation on log files and shifted structure sets according to the corresponding offsets for each beam. The dose-volume indices were calculated and compared with the original plan in terms of clinical target volume (CTV), planning target volume (CTV plus a 2-mm margin), rectum, bladder, and urethra. Prostate motions of >3, >5, and >10 mm were observed for 31.3, 9.1, and 0.5% of the 1929 timestamps, respectively. Relative differences between the simulated and original plans were mostly less than 1%. Although significant decreases were observed in D50% and D98% of the target, absolute dose differences were <0.1 Gy compared with the planned dose. The dosimetric impact of intra-fraction prostate motion may be small even with longer treatment durations, indicating that the tumour tracking using the CyberKnife could be a robust system for examining prostate motion.

Dosimetric and clinical effects of interfraction and intrafraction correlation errors during marker-based real-time tumor tracking for liver SBRT

Correlation model error (CME) between the internal target and the external surrogate, and marker-tumor correlation error (MTCE) between the tumor and the implanted marker occur during marker-based real-time tumor tracking. The effects of these intrafraction and interfraction errors on the dose coverage in the clinical target volume (CTV) and on tumor control probability (TCP) for hepatocellular carcinoma (HCC) were evaluated in this study. Eight HCC patients treated with non-isocentric dose delivery by a robotic radiosurgery system were enrolled. The CMEs were extracted from the treatment log file, and the MTCEs were calculated from the preceding study. The CMEs and MTCEs were randomly added to each beam's robot position, and the changes in the TCP and the 2%, 95% and 99% dose coverage values for the CTV (D2, D95 and D99) were simulated. The data were statistically analyzed as a function of the CTV to planning target volume (PTV) margin, the dose fraction and the marker-tumor distance. Significant differences were observed in the majority of the CTV D2, D95 and D99 values and the TCP values. However, a linear regression revealed that ∆CTV D2, D95 and D99 have a weak correlation with ∆TCP. A dose-difference metric would be unable to detect a critical error for tumor control if the coverage changes for the CTV and ∆TCP were weakly correlated. Because the simulated TCP-based parameter determination was based on the dose simulation, including predicted interfraction and intrafraction errors, we concluded that a 95th percentile TCP-based parameter determination would be a robust strategy for ensuring tumor control while reducing doses to normal structures.