Sci-Thur AM: Planning - 05: Lung SBRT: Dosimetric accuracy of the Analytical Anisotropic Algorithm (AAA) for 6MV FFF RapidArc planning
Bibliographic record
Abstract
PURPOSE: Stereotactic Body Radiation Therapy (SBRT) requires the delivery of a high biologically effective dose in only a few fractions. These large doses per fraction can necessitate long treatment times. The Varian Truebeam is capable of RapidArc delivery and also has the optional Flattening Filter Free (FFF) modes which greatly increase the dose rate. We have commissioned the 6MV FFF beam (1400 MU/min) for RapidArc lung SBRT, and verified heterogeneous dose calculations with Monte Carlo (MC). METHODS: The standard commissioning data was acquired for Varian's Analytical Anisotropic Algorithm (AAA) beam model. Measurements were acquired with the IBA Blue Phantom, using the CC13 and CC01 ion chambers and PTW diode. MLC-defined fields were also acquired for model verification. The Dosimetric Leaf Gap (DLG) was measured and then optimized using RapidArc lung SBRT plans, matching Eclipse with ion chamber measurements. Heterogeneous dose calculations were independently verified using MC. RESULTS: There were some discrepancies regarding leaf transmission and penumbra, but the AAA model was generally well within 2% and 2 mm. A nominal DLG value of 1.6 mm was chosen. A representative lung SBRT case utilizing FFF RapidArc was calculated with MC. For the high dose region, 99% matched Eclipse within 3% and 3 mm. The mean dose difference of the PTV was 0.7%. CONCLUSIONS: Although we have observed some minor infield discrepancies between the AAA and Monte Carlo calculations in heterogeneous media, the Eclipse AAA is reasonably accurate for complex FFF, RapidArc, SBRT lung planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.014 | 0.004 |
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".