Sci‐PM Sat ‐ 08: Feasibility of gated helical tomotherapy using the real‐time position management (RPM) system
Bibliographic record
Abstract
We propose a new method of gating helical tomotherapy (HT) delivery for lung lesions that does not require breath holding. Without interrupting the gantry rotation and couch translation, the complete closure of the bMLC leaves would be triggered by the Real Time Position Management (RPM) system (Varian Oncology Systems, Palo Alto, CA), whenever the associated chest (and implied tumour) position moves outside the gating window. Once the initial radiation delivery is complete, additional iterations of the helical treatment are repeated until >95 % of the planned beam projections are delivered. Each iteration commences at a different phase of the respiratory cycle to “fill in” the previously undelivered beam projections. In this study, a gated HT delivery was simulated on the HT unit by delivering a series of “modified” leaf sinograms — altered according to the RPM curve of a real lung patient. The original leaf sinogram created for a stationary target (non‐gated) was modified such that all beam projections whose delivery times fall outside the gating window were nulled. The resulting dose distribution was measured by film dosimetry, and compared to the dose distribution of the non‐gated delivery. The gated HT delivery showed excellent agreement with the non‐gated HT delivery, with a mean dose difference of less than 1% observed in the central plane. In conclusion, the feasibility of gated HT delivery using the RPM system has been demonstrated. Future work will address different breathing patterns and more complicated target shapes treated with intensity modulation.
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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 | 0.000 | 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 | 0.000 | 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".