Poster - Thurs Eve-17: Stand alone software for deforming delivered dose distributions to account for daily anatomical variations in prostate patients treated on the TomoTherapy Hi-Art II system
Bibliographic record
Abstract
The acquisition of daily megavoltage (MV)-CT images provides an invaluable tool in the delivery of adaptive radiotherapy (ART) on the TomoTherapy Hi-ART II system. Using TomoTherapy's Planned Adaptive software, delivery sinograms can be applied to pre-treatment MVCT images to generate daily delivered dose distributions, allowing for the potential comparison of planned and delivered doses. However, daily patient anatomical variations complicate the task and accurate comparison requires that daily doses be evaluated in the same references frame as the planned dose. Each anatomical point in daily MVCT images must be mapped to its corresponding point in the patient planning CT and that deformation map must be applied to the daily dose distribution. Stand alone software has been developed for the comparison of planned and delivered doses for TomoTherapy prostate patients. Software inputs are the planning CT, planning structure data, planned dose distribution, daily MVCT and delivered dose distribution. The software uses an in-house developed automatic voxel-based deformable registration algorithm designed and optimized specifically for the registration of prostate CT images to achieve anatomical correspondence between MVCT and planning images. The resultant deformation map is applied to the daily dose distribution and the software outputs the deformed daily dose distribution in the planning CT's reference frame, as well as a delivered DVH for each of the planning CT's ROI. The software allows for a number of potential research opportunities, in particular, the calculation of the cumulative dose delivered over the course of treatment for prostate patients treated on the Hi-Art II system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".