Purpose: The CyberKnife uses an online prediction model to improve radiation delivery when treating lung tumors. This study evaluates the prediction model used by the CyberKnife radiation therapy system in terms of treatment margins about the gross tumor volume (GTV). Methods: From the data log files produced by the CyberKnife synchrony model, the uncertainty in radiation delivery can be calculated. Modeler points indicate the tracked position of the tumor and Predictor points predict the position about 115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was analyzed for 100 treatment model data sets from 23 de-identified lung patients. The treatment margins were determined in each anatomic direction to cover an arbitrary volume of the GTV, derived from the Modeler points, when the radiation is targeted at the Predictor points. Each treatment model had about 30 min of motion data, of which about 10 min constituted treatment time; only these 10 min were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each direction's distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study, 95% modeler point coverage was compared to 99% modeler coverage. Two other error sources were investigated: the correlation error and the targeting error. These were added to the prediction error to give an aggregate error for the CyberKnife during treatment of lung tumors. Results: Considering the magnitude of 2r from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% modeler point coverage were 1.2 mm in the lateral (LAT) direction and 1.7 mm in the anterior-posterior (AP) direction. For the superior-inferior (SI) direction, the fit was poor; but empirically, the expansions were 3.5 mm. For 99% modeler point coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% modeler point coverage were highly variable. The aggregate error at 95% was 6.9 mm in the SI direction, 4.6 mm in the AP direction, and 3.5 in the lateral direction. Conclusions: The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% modeler point coverage for 95% of the models reviewed in this study. Similar margins were found in two clinical directions for 99% modeler point coverage in 95% of models. These results can offer guidance in the selection of CTV margins for treatment with the CyberKnife.
Fiducial marker (FM)-guided stereotactic body radiation therapy (SBRT) allows for precise targeting and delivery of radiation to a tumor site. In this article, we briefly discuss SBRT, provide examples to describe CT-guided FM placement to guide SBRT, and discuss some of the associated risks and benefits. This article serves as a pictorial review for body imagers and interventional radiologists who perform CT-guided procedures and interpret diagnostic studies for oncology patients. CT-guided FMs were placed in patients who were appropriate candidates for SBRT. One week following placement, patients underwent diagnostic CT and/or MR examinations in order to include the FM data in the development of a treatment plan. From October 2007-November 2009, a total of 89 patients were implanted with FMs. Sites of implantation included lung, liver, bone, chest and abdominal wall, and peritoneum/retroperitoneum. Complications included pneumothorax and FM migration. Twenty-one patients (33%) with lung FM placement experienced at least a small pneumothorax and 6 patients (9%) required thoracostomy tubes. FM migration occurred in 5 patients (8%) with lung placement. SBRT provides a safer and more effective alternative to conventional radiotherapy, and CT-guided FM implantation of tumor sites increases the precision of SBRT. Technical improvements in FM placement can limit the complications associated with the procedure and further enable highly localized tumor therapy.
Purpose: To evaluate the prediction model used by the CyberKnife radiation therapy system in marginal terms. Method and Materials: The CyberKnife uses an online prediction model to improve radiation delivery. Modeler points indicate the tracked position of the tumor and Predictor points predict the position ≈115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was investigated. For 100 data sets from 23 de‐identified lung patients, margins were determined in each anatomic direction about the Predictor points so as to minimally increase coverage to an arbitrary percentage of Modeler points. Each data set had about 30 minutes of motion data, of which about 10 minutes constituted treatment time; only these 10 minutes were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each direction's distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study 95% coverage was compared to 99% coverage. Results: Considering the magnitude of 2 from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% coverage were 1.2 mm in the lateral directions and 1.7 mm in the AP directions. For the SI directions the fit was poor; but empirically, the expansions were 3.5 mm. For 99% coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% coverage were highly variable. Conclusion: The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% coverage for 95% of the fractions reviewed in this study. Similar margins were found in two clinical directions for 99% coverage in 95% of fractions.
Purpose: The Synchrony Respiratory Tracking System treats moving targets with much tighter margins. Due to the complex and erratic breathing motion of lung tumors, it is a challenging task to build a reliable internal/external correlation model and predictive algorithm. This study is to assess the treatment accuracy of lung tumors using the Synchrony system. Method and Materials: 21 lung patients with 97 fractions treated by a Synchrony system were studied. Available treatment data includes (i) tumor positions from sparsely acquired x‐ray image pairs; (ii) three surface LED markers motion continuously tracked by three Infrared detectors; (iii) the modeled motion calculated using the LED motion and the internal‐external correlation model and (iv) the predicted motion to compensate system latency. The correlation models can be validated by comparing the x‐ray image pairs determined tumor positions and the calculated modeled motion. The prediction precision can be evaluated using the modeled and predicted motion. The comparison of the x‐ray image pairs determined tumor positions and the predicted motion will assess the treatment effectiveness and provide feedback for the optimized margin expansion, computed using either a rolling ball method or un‐even margin expansions in X, Y, and Z directions. Results: Preliminary analysis showed the predictive model worked well with fast motion during inhale and exhale and decreased at the end of inhale and the end of exhale, especially at the reflection points. The fidelities of the correlation model changes over time, with correlation error up to 11.5mm in 3D distance for some fraction. The margin expansions required for different coverage percentages were calculated using both the rolling ball approach and un‐even expansion for each treatment direction. Conclusion: Evaluation of the correlation model, predictive results, and margin expansions were performed and the results are important to improve lung tumor treatments using the Synchrony System.
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