Poster — Thur Eve — 50: Planning and delivery accuracy of stereotactic radiosurgery with Tomotherapy as compared to linear‐accelerator and robotic based radiosurgery
Bibliographic record
Abstract
This study includes planning and delivery comparison of three stereotactic radiosurgery techniques : Helical Tomotherapy (HT), circular collimator‐based Linear‐accelerator and robotic‐radiosurgery. Plans were generated for two spherical targets of diameter 6 mm and 10 mm contoured at the center of a Lucite phantom, using similar planning constrains. Planning comparison showed that average conformality (0–1best) for Linear‐accelerator, robotic‐radiosurgery and HT was 1.43, 1.24, and 1.77 and gradient index (less is better) was 2.72, 4.50 and 13.56 respectively. For delivery comparison, plans were delivered to radiochromic film and measured dose was compared with the planned dose. For Linear‐accelerator and robotic‐radiosurgery more than 99% pixels‐passing a gamma criteria of 3% dose difference and 1 mm distance to agreement where as for HT this value was as low as 40% for off‐axis targets. Further investigation of the delivery accuracy as a function of the location of the target with in the bore was initiated using small volume A1SL (0.057 cm3) and MicroLion liquid ion chamber (0.0017 cm3). Point dose measurements for targets located at the center and 10 cm away from the center of the bore showed that delivered dose varied by more than 15% for targets placed away from the center of the bore as opposed to at the center. In conclusion, Linear‐accelerator and the robotic‐radiosurgery techniques showed preferable gradient and conformality. For HT, point dose measurements were significantly lower than predicted by the TPS when the target was positioned away from the isocenter, while they were found to be higher at isocenter.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".