Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife
Bibliographic record
Abstract
PURPOSE: Several studies have demonstrated potential improvements in treatment time through the use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife. However, the delivery system has a finite accuracy, so that potential exists for dosimetric uncertainties. This study estimates the expected dosimetric accuracy of dynamic delivery of SBRT, based on realistic estimates of the uncertainties in delivery parameters. METHODS: Five SBRT patient cases (prostate A - conventional, prostate B - brachytherapy-type, lung, liver, partial left breast) were retrospectively studied. Treatment plans were produced for a fixed arc trajectory using fluence optimization, segmentation, and direct aperture optimization. Dose rate uncertainty was modeled as a smoothly varying random fluctuation of ± 1.0%, ±2.0% or ± 5.0% over a time period of 10, 30 or 60 s. Multileaf collimator uncertainty was modeled as a lag in position of each leaf up to 0.25 or 0.5 mm. Robot pointing error was modeled as a shift of the target location, with the direction of the shift chosen as a random angle with respect to the multileaf collimator and with a random magnitude in the range 0.0-1.0 mm at the delivery nodes and with an additional random magnitude of 0.5-1.0 mm in between the delivery nodes. The impact of the errors was investigated using dose-volume histograms. RESULTS: Uncertainty in dose rate has the effect of varying the total monitor units delivered, which in turn produces a variation in mean dose to the planning target volume. The random sampling of dose rate error produces a distribution of mean doses with a standard deviation proportional to the magnitude of the dose rate uncertainty. A lag in multileaf collimator position of 0.25 or 0.5 mm produces a small impact on the delivered dose. In general, an increase in the PTV mean dose of around 1% is observed. An error in robot pointing of the order of 1 mm produces a small increase in dose inhomogeneity to the planning target volume, sometimes accompanied by an increase in mean dose by around 1%. CONCLUSIONS: Based upon the limited data available on the dose rate stability and geometric accuracy of the Cyberknife system, this study estimates that dynamic arc delivery can be accomplished with sufficient accuracy for clinical application. Dose rate variation produces a change in dose to the planning target volume according to the perturbation of total monitor units delivered, while multileaf collimator lag and robot pointing error typically increase the mean dose to the planning target volume by up to 1%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".