TH‐EF‐BRB‐06: Implementation of a Modulated‐Arc Total Body Irradiation (TBI) Technique Using the RayStation Treatment Planning System
Bibliographic record
Abstract
Purpose: To develop a clinical workflow for delivering a modulated‐arc total body irradiation (TBI) with RayStation scripting. This technique uses arc fields with the patient lying at floor level on a padded table and is validated through measurements taken on a custom‐made TBI phantom. Methods: Treatment planning was performed for a retrospective cohort of eight patients with a diverse range of heights and body types. Each was replanned using an open‐field dual arc method, with the patient in supine and prone positions on the floor of the vault. All plans were optimized using Raystation Planning 4.7.2.5 (RaySearch Laboratories, Stockholm, Sweden), with 200 cGy prescribed to the 95% of the body contour − 5mm. This results in an open‐field beam that sweeps craniocaudally across the length of the patient. The technique is validated with measurements at 10 cm intervals in a custom‐milled, 5 cm thick acrylic phantom. A centrally located CC13 ion chamber and a Mobile MOSFET (Best Medical Canada, Ottawa, ON) detector array were used to measure dose. Supine and prone arcs for each patient were consecutively delivered, and the aggregate dose at each point was compared to the planned dose calculated in the phantom. Results: The ion chamber measurements differed from the planned dose by an average of .5%, with a standard deviation of 2.1%. All measured data for the MOSFETS were within 10% of the corresponding planned dose except for two outlying points. The standard deviation of dose differences across the entire cohort was 4.0%. Most significant discrepancies occurred either in inhomogeneous regions with large gradients, or at inferior points where beam angle was steepest. Conclusion: We have confirmed that the planned dose is well matched to our measurements within 10% for this method of planning and delivery. We are currently incorporating this technique into our clinical workflow. This work is supported by RaySearch.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".