Research ArticleClinical Studies

Evaluation of Lung and Liver Tumor Dose Coverage Treated With the CyberKnife Synchrony System With Consideration of Measured Tracking Errors

YUICHI AKINO, HIROYA SHIOMI, NAOICHI HIGASHINAKA, TOMOHIRO KOUNO, NOBUHISA MABUCHI, FUMIAKI ISOHASHI, YUJI SEO, KEI FUJIWARA, SETSUO TAMENAGA and KAZUHIKO OGAWA
Anticancer Research January 2023, 43 (1) 231-238; DOI: https://doi.org/10.21873/anticanres.16154

Abstract

Background/Aim: Lung and liver tumor dose coverage was evaluated for the CyberKnife synchrony respiratory tracking system (SRTS) with consideration of the motion tracking accuracy measured for motion patterns of individual patients. Patients and Methods: Seven treatment plans of six cases treated with the SRTS were evaluated. The motion phantom was moved with the motion data derived from the treatment log files. A laser emitted from the linac head to the moving phantom block was recorded with a webcam, and the tracking accuracy was evaluated. The dose volume histogram (DVH) of planning target volume (PTV) and gross tumor volume (GTV) were calculated by a pencil beam algorithm with shifting the beams with Gaussian random numbers mimicking the measured tracking errors. Results: The tracking errors measured with the motion phantom in the lateral direction were within ±2 mm for 90% of beam-on time. The tracking errors in the longitudinal direction were within ±3.0 mm and ±1.1 mm for 90% and 50% of beam-on time, respectively. Although one case showed a decrease in the dose covering 95% of PTV (D95%) by 1.8%, the change in the dose covering 99% of GTV (D99%) was within 1%. Conclusion: This study evaluated the motion tracking errors of the SRTS by a motion phantom moved with the patients’ respiration signal, and the impact of the tracking errors on the target coverage was calculated. Even for respiratory patterns with large maximum tracking errors, sufficient GTV coverage is achievable if the beam is accurately delivered for high percentage of beam-on time.

Key Words:

Stereotactic body radiotherapy (SBRT) for lung and liver tumors has shown good clinical outcomes (1, 2). For SBRT, conformal beam delivery is important for reducing the risks of pulmonary pneumonitis and radiation-induced liver disease caused by irradiation to surrounding healthy tissue. The CyberKnife robotic radiosurgery system (Accuray, Inc., Sunnyvale, CA, USA) enables a high conformal dose distribution by irradiation of multiple non-coplanar beams using the linear accelerator mounted on the robotic arm (3). For moving targets including lung and liver tumors, a synchrony respiratory tracking system (SRTS) minimizes irradiation to surrounding healthy tissue by tracking the tumors moving with respiration (4, 5). A target-locating system (TLS) is used for localization of the skull, spine, and fiducial markers (3, 6). For lung tumors detectable clearly on the images acquired with the TLS, an orthogonal kilovoltage image-guided radiotherapy system, a lung optimized treatment (LOT), enables tracking irradiation of the moving tumor without implantation of fiducial markers (7).

Many studies have investigated the motion tracking accuracy of the SRTS using various techniques: detection of the laser indicating the radiation beam path emitted from the linac head (8), video capture using a camera mounted on the linac head (9), measurements using Radiochromic film and motion phantom (10), measurements of photon beams using a plastic scintillator (11, 12), analysis of the log files automatically saved after treatment (13-16), and machine learning (17). However, few studies have investigated the target coverage affected by the tracking accuracy of the SRTS evaluated by measurements (18, 19).

In this study, we investigated the motion tracking accuracy of the SRTS of each patient using a three-dimensional (3D) motion phantom moved with the respiratory motion signal of the tumor and abdominal surface derived from the treatment log files. In addition, the dose volume histogram (DVH) of the target was evaluated with consideration of the tracking errors of the SRTS assessed by the measurements.

Patients and Methods

The DVH analysis with consideration of the tracking errors of the SRTS was performed with the following steps: (i) Evaluation of the 3D tumor movement from the treatment log files, (ii) measurements of the tracking accuracy of the SRTS using the 3D motion phantom, (iii) generation of Gaussian random numbers fitted with the measured tracking errors, and (iv) calculation of the DVH with consideration of the tracking errors.

Motion data of patients. We evaluated seven treatment plans of six cases treated with the SRTS. The CyberKnife G4 system was used for the treatments. The details of the cases and treatments are listed in Table I. The patients’ breathing signal and 3D tumor motion were derived from the light-emitting diode (LED) marker position data (Markers.log) and the correlation model data (Modeler.log). These log file data were combined with reference to the time stamp and resampled to the temporal resolution of 0.1 s to use it for the motion phantom.

View this table:
Table I.

Tumor location and details of the treatment.

Phantom measurements. A 3+1 axes motion phantom (Enomoto BeA Co., Ltd, Kagamihara, Japan) was used (Figure 1A). The phantom consisted of a main 3D motion arm and an extra moving stage mimicking the vertical motion of the patient abdomen surface. One LED marker was placed on the vertical motion stage. A wooden block with three metallic spheres was attached on the main arm. To identify the position of the laser emitted from the linac head, a black paper sheet with four white markers was attached to the top of the wooden block (Figure 1B). The white markers represent the corners of a 40×40 mm2 square. The CT images of the block were acquired to generate an isocentric treatment plan using a MultiPlan™ (ver. 4.6.0,) treatment planning system (TPS). The treatment plan contained multiple beams irradiating a target, which was virtually generated near the center of the square on the block surface. Some beams with very shallow beam angles were removed, and 25 remaining beams were used. The monitor units (MUs) of each beam were 300 MU.

Figure 1.
Figure 1.

(A) Schematic image of the motion phantom for evaluation of the motion tracking accuracy of the synchrony respiratory tracking system (SRTS). (B) A photo of the paper sheet attached on the wooden block (upper panel) and the dimensions of the white markers (lower panel).

A web camera (C920 HD Pro, Logitech, Lausanne, Switzerland) was placed on the motion phantom to capture the moving wooden block (Figure 1). In-house software was used for the analysis of the captured video. The contrast of the images was adjusted to clearly show the four white markers, and the markers were then manually identified on one of the frames. The marker positions on all other frames were automatically detected by setting region of interests (ROIs) slightly larger than the markers at the positions on the previous frame. When the laser was irradiated from the linac head, a red spot was shown near center of the square. A ROI sized at 80% of the square was set to detect the laser. A beam number was assigned to each frame on which the laser was observed inside the ROI. The beam number was incremented when the laser was not detected for >1 s. Although the CyberKnife head stops tracking of the target immediately after reaching the planned irradiation time, the laser turns off with a slight delay. From the laser-detected image set of each beam, image frames corresponding to the last 2 s were ignored.

Although the video was captured from the oblique direction, the positions of the four white markers were known as the corners of the 40×40 mm2 square. For each frame, a homography matrix for the correction of distortion was calculated, and the position of the laser detected inside the square was corrected with the homography matrix.

Even if the SRTS system achieved perfectly accurate irradiation with the laser, the position of the laser would be affected by (i) the positional accuracy of target generation on the TPS and (ii) distortion of the laser spot on the wooden phantom owing to the oblique angle. To eliminate these effects, the measurement was also performed with the main arm of the phantom stationary (Figure 2A, upper panel). For each beam, the mean displacement from the origin was calculated in the lateral and longitudinal directions. The positions of the laser were measured with the respiratory motions of the main arm (Figure 2A, lower panel) and corrected with the mean displacement of each beam (Figure 2B, lower panel).

Figure 2.
Figure 2.

(A) The coordinates of the laser positions detected with the camera without (upper panel: Static) and with (lower panel: SRTS) phantom movement. (B) The data shown in panel A, after being corrected with the data measured with the phantom stationary (A, upper panel). SRTS, Synchrony respiratory tracking system; LAT, lateral axis; LONG, longitudinal axis.

Gaussian random numbers representing tracking errors. The tracking errors of the SRTS were analyzed in the lateral and longitudinal directions. Figure 1C shows an example of the distribution of the tracking errors in the longitudinal direction measured for five treatment fractions. For each treatment fraction, the mean and standard deviation (SD) of the tracking errors were analyzed using the JMP PRO 14.0 (SAS Institute, Cary, NC, USA) software. To simulate the tracking errors occurring during irradiation, Gaussian random numbers were generated for the SD calculated for each treatment fraction.

Evaluation of target dose coverage with consideration of tracking errors. A ShioRIS 2.0 software was used to evaluate the DVH of the targets with consideration of the motion tracking errors. The ShioRIS 2.0 calculates dose distribution of the CyberKnife beams with a pencil beam algorithm. The calculation accuracy of the software has been reported elsewhere (20). To calculate DVH, the target volume was segmented into voxels, and the dose delivered to each voxel was calculated for each beam. The DVH with consideration of the tracking errors was evaluated by shifting the beams. The displacement was calculated by Gaussian random numbers, as described above. Because usually each beam is delivered during >1 breathing cycle, we assumed that the tracking errors follow a Gaussian distribution. For each beam, 100 Gaussian random numbers were generated for both lateral and longitudinal directions, and a mean of 100 calculations was assigned to each voxel. For each voxel, the mean dose delivered from all beams were summed to calculate the total dose delivered during the treatment fraction.

Results

Figure 3 shows the 3D motion amplitude and velocity of the tumors derived from the log files recorded for the patient treatments. For each treatment fraction, the range of the motion amplitude and the maximum velocity were calculated for 99% of data points (range from 0.5th to 99.5th percentile). The motion amplitude was largest in the superior-inferior (SI) direction for all cases, whereas the amplitude in the lateral (LR) direction was the smallest and within 5 mm. For some cases, a motion amplitude >10 mm was observed in the anterior-posterior (AP) direction. The motion velocity showed similar data.

Figure 3.
Figure 3.

Motion amplitude (upper panels) and velocity (lower panels) of the target evaluated with the treatment log files. Points and bars represent mean and range of all treatment fractions. LR, Lateral; AP, anterior-posterior; SI, superior-inferior.

Figure 4 shows the box-whisker plot of the motion tracking errors measured with the motion phantom. For all cases, the maximum absolute tracking errors were on average 2.5 mm (range=1.1-4.1 mm) and 5.7 mm (range=3.6-9.1 mm) for lateral and longitudinal directions, respectively. For 90% of beam-on time (range from 5th to 95th percentile), however, the absolute tracking errors were 1.2 mm (range=0.4-2.0 mm) and 2.1 mm (range=1.1-3.0 mm) for lateral and longitudinal directions, respectively. For 50% of beam-on time (range from 25th to 75th percentile), the maximum absolute errors were within 1.1 mm for both the lateral and longitudinal directions.

Figure 4.
Figure 4.

Box-whisker plot of the motion tracking errors measured with the motion phantom. The combined data of all treatment fractions are shown.

Figure 5 shows example DVHs of case #1. The DVH of the planning target volume (PTV) showed clear decrease of the target coverage from the original one when considering the motion tracking errors. In contrast, the differences of the DVH curves of the gross target volume (GTV) were negligibly small.

Figure 5.
Figure 5.

An example DVH of case #1. Horizontal axis represents the percent prescribed dose. Original data represent the DVH evaluated without tracking errors. Frac, Fraction.

Figure 6 shows the difference in the dose covering 95% of PTV (PTV D95%), the dose covering 99% of GTV (GTV D99%), and the dose delivered to 2% of GTV (GTV D2%) of the plans with consideration of the tracking errors from the values of the original plans. Points and bars represent the mean and range of all treatment fractions. Cases #1, #4, and #7 showed a clear decrease in PTV D95%. In contrast, the difference in GTV D99% was within 1.2% for all treatment fractions, and mean GTV D99% was within 0.5% for all cases. The difference in GTV D2% was within 1% for all treatment fractions.

Figure 6.
Figure 6.

DVH (dose volume histogram) parameters of the target evaluated with consideration of the tracking errors. The differences from the value without tracking errors are shown. Plots and bars represent the mean and range of all treatment fractions. PTV, Planning target volume; GTV, gross tumor volume; D95%, dose covering 95% of PTV; GTV D99%, the dose covering 99% of GTV; GTV D2%, the dose delivered to 2% of GTV.

Discussion

In this study, the motion tracking accuracy of the SRTS was evaluated using the 3D motion phantom, moved with the patients’ respiratory tumor motion. In addition, the DVH of the target was evaluated with consideration of the tracking errors. Some patients showed large tracking errors >5 mm in the SI direction. However, the motion tracking errors were within ±3 mm for 90% of beam-on time, and the errors were within ±1.1 mm for 50% of beam-on time. Although some patients showed significant decrease of the PTV coverage, the GTV of all patients did not show any significant decrease. Chan et al. previously evaluated the motion tracking accuracy of liver tumors and reported that the median PTV coverage decreased by 1.1% (range=−7.8%-+0.8%), whereas the change in the CTV coverage was 0.0% (range=−1.0%-+5.4%) (18). The DVH data of the current study also showed similar results, indicating that the impact of large tracking errors exceeding the PTV margin on the coverage of GTV would be small if a high percentage of the beam was delivered with small tracking errors. A significant decrease of PTV D95% was observed for cases #1, #4 and #7. However, this trend was not correlated with the tumor motion amplitude, velocity, and the volume of PTV. The impact of the tracking errors could depend on various other factors including the shape of the target and gradient of the dose distribution.

Many previous studies have investigated the motion tracking accuracy of the SRTS. Because most of them analyzed the treatment log files (14, 16, 21, 22), however, some uncertainties related to the mechanical accuracy and image guidance may not be considered. We previously developed a method to detect the position irradiated by X-ray beam using a plastic scintillator plate (11, 12). This method enabled evaluation of composite uncertainties including mechanical accuracy and modeling of breathing patterns. However, the phantom motion was limited in the LR and SI directions, although the tumor motion in the AP direction has been reported to be larger than that in the LR direction (23-25). The current study also showed similar data of the motion amplitude (Figure 3). The method developed in this study enabled three-dimensional phantom motion including vertical axis using the wooden block and laser emitted from the linac head. Therefore, tracking errors of more realistic tumor motions were evaluated. Sumida et al. previously reported similar measurements, although the motion patterns used for phantom measurements were the same for all LR, AP, SI, and surrogate marker motions (8). Yoshioka et al. recently evaluated the DVH of the liver tumors treated with the SRTS with consideration of the tracking errors (19). They evaluated the tumor motion with the area-detector 4D-CT and estimated the tracking errors of the SRTS from literature. This study enabled more realistic evaluation of the tracking errors for individual cases and enabled DVH analysis with consideration of the tracking errors evaluated by direct measurements.

The current study has certain limitations. Firstly, the 3D tumor motion data used for phantom measurements were generated from the treatment log files, which contain uncertainties of modeling of the respiratory patterns. Secondly, the method developed in this study evaluated only the tracking accuracy of the SRTS, because the process of correcting the laser position with that measured for the phantom stationary would compensate the systematic errors in irradiation of the static target. Such systematic errors should be separately evaluated with other quality assurance tests such as AQA and E2E (26). Finally, the ShioRIS 2.0 software calculates the dose distribution with the pencil beam algorithm. Because the algorithm does not consider lateral electron transport in heterogeneous tissue, the dose calculation accuracy of the lung tumor cases (case #1 and #6) would be inferior to the Monte-Carlo calculations. Because the original DVHs were also re-calculated with the pencil beam algorithm, the impact of the tracking errors on the target coverage could be estimated by comparing the DVHs with tracking errors with the original one.

Conclusion

This study evaluated the motion tracking errors of the SRTS using a 3D motion phantom moved with the patient respiration signal. In addition, the impact of the tracking errors on the target coverage was calculated. Even for respiratory patterns with large maximum tracking errors, sufficient GTV coverage is achievable if the beam is accurately delivered for high percentage of beam-on time.

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP20K08022.

Footnotes

  • Authors’ Contributions

    Conceptualization and investigation: Yuichi Akino and Hiroya Shiomi; Funding: Yuichi Akino; Data acquisition: Yuichi Akino, Naoichi Higashinaka, and Tomohiro Kouno; Supervision: Nobuhisa Mabuchi, Fumiaki Isohashi, Yuji Seo, Kei Fujiwara, Setsuo Tamenaga, and Kazuhiko Ogawa.

  • Conflicts of Interest

    YA is a developer of the software controlling the 3D motion phantom. HS is a developer of the ShioRIS 2.0 software.

  • Received November 16, 2022.
  • Revision received November 29, 2022.
  • Accepted November 30, 2022.

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Evaluation of Lung and Liver Tumor Dose Coverage Treated With the CyberKnife Synchrony System With Consideration of Measured Tracking Errors
YUICHI AKINO, HIROYA SHIOMI, NAOICHI HIGASHINAKA, TOMOHIRO KOUNO, NOBUHISA MABUCHI, FUMIAKI ISOHASHI, YUJI SEO, KEI FUJIWARA, SETSUO TAMENAGA, KAZUHIKO OGAWA
Anticancer Research Jan 2023, 43 (1) 231-238; DOI: 10.21873/anticanres.16154
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