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

Evaluation of a New Method for CyberKnife Treatment for Central Lung and Mediastinal Tumors by Tracheobronchial Tracking

BACKGROUND

CyberKnife treatment for central lung tumors and mediastinal tumors can be difficult to perform with marker less.

PURPOSE

We aimed to evaluate a novel tracheobronchial-based method (ie, tracheobronchial tracking) for the purpose of minimally invasive CyberKnife treatment for central lung and mediastinal tumors.

METHODS

Five verification plans were created using an in-house phantom. Each plan included five irradiation sessions. The reference plan irradiated and tracked the simulated tumor (using the target tracking volume, TTV). Trachea plans tracked the simulated tracheo-bronchus and irradiated the simulated tumor and included two types of subplans: correlated plans in which the displacement of the simulated tracheobronchial and the simulated tumor were correlated, and non-correlated plans in which these factors were not correlated. Moreover, 15 mm and 25 mm TTVs were evaluated for each plan. The sin waveform and the patient's respiratory waveform were prepared as the respiratory model. Evaluations were performed by calculating the dose difference between the radiophotoluminescent glass dosimeter (RPLD)-generated mean dose values (generated by the treatment planning system, TPS) and the actual absorbed RPLD dose. Statistical analyses were performed to evaluate findings for each plan. Correlation and prediction errors were calculated for each axis of each plan using log files to evaluate tracking accuracy.

RESULTS

Dose differences were statistically significant only in comparisons with the non-correlated plan. When evaluated using the sin waveform, the mean values for correlation and prediction errors in each axis and for all plans were less than 0.6 mm and 0.1 mm, respectively. In the same manner, they were less than 1.1 mm and 0.2 mm when evaluated using the patient's respiratory waveform.

CONCLUSION

Our newly-developed tracheobronchial tracking method would be useful in facilitating minimally invasive CyberKnife treatment in certain cases of central lung and mediastinal tumors.

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

BACKGROUND

In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model.

METHODS

Seven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance.

RESULTS

Overall, tuning the hyperparameters results in a 25%-30% improvement for all models compared to previous studies. The comparison between all models also shows that the gated recurrent unit (GRU) with RMSE = 0.108 ± 0.068 mm predicts respiratory signals with higher accuracy and better performance.

CONCLUSION

Overall, tuning the hyperparameters in the GRU model demonstrates a better result than the hybrid predictor model used in the CyberKnife VSI system to compensate for the 115 ms system latency. Additionally, it is demonstrated that the tuned parameters have a significant impact on the prediction accuracy of each model.

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.

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

Background

The objective was to investigate the change in segmentation error of Radixact^® Synchrony^® lung treatment after its kV imaging system was upgraded from Generation 1 to Generation 2 in the ClearRT™ installation.

Materials and methods

Radixact^® Lung Synchrony^® plans were created for the Model 18023 Xsight^® Lung Tracking "XLT" Phantom combined with different lung target inserts with densities of 0.280, 0.500, 0.943 and 1.093 g/cc. After Radixact^® Synchrony^® treatment delivery using the Generation 1 and Generation 2 kV systems according to each plan, the tracking performance of the two kV systems on each density insert was compared by calculating the root mean square (RMS) error (δ_RMS) between the Synchrony-predicted motion in the log file and the known phantom motion and by calculating δ95%, the maximum error within a 95% probability threshold.

Results

The δ_RMS and δ95% of Radixact^® Synchrony^® treatment for Gen1 kV systems deteriorated as the density of the target insert decreased, from 1.673 ± 0.064 mm and 3.049 ± 0.089 mm, respectively, for the 1.093 g/cc insert to 8.355 ± 5.873 mm and 15.297 ± 10.470 mm, respectively, for the 0.280 g/cc insert. In contrast, no such trend was observed in the δ_RMS or δ95% of Synchrony^® treatment using the Gen2 kV system. The δ_RMS and δ95%, respectively, fluctuated slightly from 1.586 to 1.687 mm and from 2.874 to 2.971 mm when different target inserts were tracked by the Gen2 kV system.

Conclusion

With improved image contrast in kV radiographs, the Gen2 kV imaging system can enhance the ability to track targets accurately in Radixact^® Lung Synchrony^® treatment and reduce the segmentation error. Our study showed that lung targets with density values as low as 0.280 cc/g could be tracked correctly in Synchrony treatment with the Gen2 kV imaging system.