SU-E-T-330: Achieving a Uniform Breast Junction with the Elekta Agility
Bibliographic record
Abstract
Purpose: Assessment of a clinical method that insures a uniform junction when treating four field breast cases on Elekta Synergy linacs outfitted with the new Agility 160 leaf multileaf collimator. Methods: Numerous breast patient step-and-shoot plans were generated using Philips Pinnacle3 treatment planning system. Calculated doses were compared with measurements done using Gafchromic EBT3 film inserted in a plastic water phantom.The linac beam spot position was measured according to one of the recently published methods by Balazs et al. (2012). The jaws and MLC leaves were calibrated using the portal imager following the calibration workflow integrated into the linac console. The resulting junction was evaluated using two half blocked fields on XV film at gantry 0°. Results: Initial measurements showed slight variations in beam spot position between all three linacs. These variations were the main factor causing inconsistent and cold junctions. The beam spot adjustment followed by the collimator calibration workflow improved field junctions on the XV film to within tolerance for all linacs.Patient plans in 3 showed that a beam overlap was needed to achieve adequate coverage of the treatment region on the CT slices located close to the junction. In order to avoid overlapping fields at the skin, only the external tangent and the posterior field were overlapped.Gafchromic film measurements showed that a 2 mm beam overlap for a minimum of 5% of the monitor units of both overlapping beams was required to produce a uniform junction within 5% of Pinnacle3 dose calculation. Conclusion: In order to achieve a uniform junction, fine tuning of the beam spot position was necessary. A dosimetric method was developed to achieve uniform dose distribution in the junction area when treating four field breast cases on an Elekta Synergy linac with the new Agility 160 leaf multileaf collimator.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".