SU‐E‐T‐429: Delivery Accuracy of Stereotactic Radiosurgery with Tomotherapy Using Treatment Planning System Version V4.0 and the Recent Upgrade V4.2
Bibliographic record
Abstract
Purpose: To compare the delivery accuracy of helical tomotherapy for stereotactic radiosurgery using two treatment planning system (TPS) versions, v4.0 and the recent upgrade that includes increased sampling in the dose calculation algorithm v4.2. Methods: Two spherical targets of diameter 6 mm and 10 mm were contoured on the CT scan of a Lucite phantom. Three sets of plans (targets positioned at the machine isocenter and 10 cm to both anterior and lateral to the machine isocenter) were generated, with both TPS v4.0 and v4.2 using Fine dose grid resolution of 2×2×1 mm3. Radiochromic film was used to measure the dose profiles and two ionization chambers (A1SL and MicroLion) were used to measure the point dose. Results: The agreement between delivered and calculated dose was inferior for v4.0 compared to v4.2. For both planning versions, the measured dose for the targets placed at the isocenter was higher than the planned dose whereas for off‐axis targets, v4.2 showed higher measured dose and v4.0 showed lower measured dose compared to the planned dose. For v4.0, the percentage dose difference varies 13%to 24%(depending upon the detector) when the targets were placed at 10 cm away from the machine isocenter instead of at the isocenter, whereas for v4.2 this range was 3% to 6%. Conclusion: The agreement between delivered and calculated dose depends upon the position of the target inside the treatment bore. The higher dose delivered at isocenter with both planning versions could be due to the relatively large size of smallest dose grid available at tomotherapy TPS. A further investigation to determine the cause of higher dose at isocenter is required and is ongoing.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".