SU‐GG‐T‐267: Evaluation of the Accuracy of TomoTherapy Dose Calculations for Shallow‐Depth PTVs
Bibliographic record
Abstract
Purpose: To investigate the accuracy of TomoTherapy dose calculations for shallow‐depth PTVs, and to examine the sensitivity of the resulting plans to positional variations. Method and Materials: TomoTherapy treatment plans were created and delivered for a cylindrical phantom with surface‐to‐PTV margins of 0 to 10 mm in 2 mm increments. Dose in the coronal plane was measured using EDR2 film and compared to the dose predicted by the TomoTherapy software. Treatment was also delivered with the phantom intentionally misaligned to investigate the sensitivity of each plan to imperfect patient alignment. Results: A margin of 0mm resulted in an excess dose on the order 15% to the region just below the surface of the phantom. This was consistent with the dose predicted by the planning software. Using a PTV to skin margin of 2mm or more resulted in only a negligible overdose to the phantom. With a margin of 0mm, the dose delivered was very sensitive to misalignment of the phantom. Moving the phantom by 4mm in one direction resulted in a peak dose slightly higher than predicted; moving it 4mm in the other direction substantially reduced the peak dose, nearly eliminating the overdosing altogether. With a margin of 4mm, misalignment had only negligible impact on the maximum dose. Conclusion: The results suggest that when treating breast cancer using tomotherapy, the PTV should be kept at least 2mm back from the surface of the skin to avoid severe dose dumping just below the skin and a maximum dose that changes significantly with only small misalignment of the patient.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".