A case study for online plan adaptation using helical tomotherapy
Bibliographic record
Abstract
Helical tomotherapy's ability to provide daily megavoltage (MV) computed tomography (CT) images for patient set-up verification allows for the creation of adapted plans. As plans become more complex by introducing sharper dose gradients in an effort to spare healthy tissue, inter-fraction changes of organ position with respect to plan become a limiting factor in the correct dose delivery to the target. Tomotherapy's planned adaptive option provides the possibility to evaluate the dose distribution for each fraction and subsequently adapt the original plan to the current anatomy. In this study, 30 adapted plans were created using new contours based on the daily MVCT studies of a bladder cancer patient with considerable anatomical variations. Dose to the rectum and two planning target volumes (PTVs) were compared between the original plan, the dose that was actually delivered to the patient, and the theoretical dose from the 30 adapted plans. The adaptation simulation displayed a lower dose to 35% and 50% of the rectum compared to no adaptation at all, while maintaining an equivalent dose to the PTVs. Although online adaptation is currently too time-consuming, it has the potential to improve the effectiveness of radiotherapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".