Verification of dynamic intensity‐modulated beam deliveries in canine subjects
Bibliographic record
Abstract
Our objective in this work was to assess the precision and degree of accuracy with which intensity modulated radiation therapy (IMRT) can deliver highly localized dose distributions to tumors near critical structures using the dynamic sliding window technique. Measurements of dose distribution were performed both in vivo and in vitro using a combination of dosimeters [thermoluminescent dosimeters (TLD's), films, and diodes]. In vivo measurements were performed in two groups of purpose-bred dogs: one receiving four-field three-dimensional (3D) conformal treatment and the other receiving IMRT. The algorithms used in the inverse planning process included the Macro Pencil Beam (MPB) model and Projections onto Convex Sets (POCS). Single beam measurements were performed in phantoms to verify the accuracy of monitor unit settings required for delivering the desired doses. The composite doses from the delivery of the seven beam intensity modulated plans were measured in phantoms and cadavers, Biological end points (spinal cord toxicity and neurologic deficits due to irradiation) were evaluated at the end of one year to determine the spatial accuracy of the IMRT treatments over a fractionated course in live subjects. Results in single beam measurements were used at first to improve the dose calculation and translation algorithms. Results of the measurements for the delivery of all seven beams in phantoms confirmed that the system was capable of accurate spatial and dosimetric IMRT delivery. The in vivo results showed dramatic differences between control and IMRT-treated dogs, with the IMRT group showing no adverse effects and the control animals showing severe spinal cord injuries due to irradiation. The measurements presented in this paper have helped to verify the successful and accurate delivery of IMRT in a clinically related model using the University of Washington Medical Center (UWMC) 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) | 0.000 | 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) | 0.001 | 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".