Poster — Wed Eve—45: Imaging and Radiation Delivery in Helical Tomotherapy: Phantom Study of a Moving Target
Bibliographic record
Abstract
Radiation is an effective method of treating lung cancer, however, the tumour moves while the patient breathes. To ensure radiation always covers the tumour, a volume larger than the target must be treated. For conventional treatments, gating can be employed, but helical tomotherapy treatments cannot be gated. We hypothesized that, through judicious choice of planning computed tomography (CT) imaging methods, radiation can be accurately planned and delivered to a moving tumour using tomotherapy. A breathing phantom was used which allowed one‐dimensional motion of a lung‐equivalent cylinder containing a target. By varying the imaging parameters, we obtained numerous different planning studies with target motion in both the superior‐inferior and lateral directions. These studies included a static study, fast‐CT studies, a maximum intensity projection (MIP), an average intensity projection (AveIP) and an untagged average study. Planning studies were acquired with the cylinder moving sinusoidally with a period of 4 s and amplitude of 1 cm. Treatment plans were created for each CT study and delivered using tomotherapy. Dose was measured using self‐developing film. Dose‐area histograms were used to compare the dose delivery to the central coronal plane of the target for all planning studies. A similar experiment was also performed using an irregular breathing pattern. Results indicated that planning using the AveIP study results in the most accurate treatment of a moving target. The amount of dose delivered to the normal tissue did not change significantly, due to the small increase in irradiated area compared to the entire area of the film.
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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) | 0.007 | 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".