Patient specific treatment verifications for helical tomotherapy treatment plans
Bibliographic record
Abstract
We performed two-dimensional treatment verifications for ten patients planned and treated with helical tomotherapy. The treatment verification consisted of a film measurement as well as point dose measurements made with an ion chamber. The agreement between the calculated and the measured film dose distributions was evaluated with the gamma index calculated for three sets of criteria (2 mm and 2%, 4 mm and 3%, and 3 mm and 5%) as recommended in the literature. Good agreement was found between measured and calculated distributions without any need of normalization of the dose data but with dose map registration using reference marks. In this case, 69.8 +/- 17.2%, 92.6 +/- 9.0%, and 93.4 +/- 8.5% passed the 2 mm and 2%, 4 mm and 3%, and 3 mm and 5% criteria, respectively. Agreement was excellent when both normalization and manual registration of the dose maps was employed. In this case 91.2 +/- 5.6%, 99.0 +/- 1.4%, and 99.5 +/- 0.8% passed the 2 mm and 2%, 4 mm and 3%, and 3 mm and 5% criteria, respectively. The mean percent discrepancy for the point dose measurements was -0.5 +/- 1.1%, -2.4 +/- 3.7%, -1.1 +/- 7.3% for the high dose, low dose, and critical structure point, respectively. Three criteria for a satisfactory treatment verification in the high dose regions of a plan were established. For the un-normalized reference mark registered data 80% of pixels must pass the 3 mm and 5% criteria. For the normalized and manually registered data, 80% must pass the 2 mm and 2% criteria, and the point dose measurement must be within 2% of the calculated dose. All low dose region/critical structure point dose measurements were evaluated on a patient by patient basis. The criteria we recommend can be useful for the routine evaluation of treatment plans for tomotherapy systems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".