SU-E-T-602: Investigation of Target Motion for Serially Delivered TMI Treatments
Bibliographic record
Abstract
Purpose: Total marrow irradiation (TMI) delivered via helical TomoTherapy (HT) presents a unique challenge for organ localization because the entire target structure (skeletal bone) cannot be imaged and registered with the planning CT accordingly. Therefore a few surrogates must be used to determine the position of many structures along the entire length of the patient. We investigated the CTV to PTV margin sizes required in order to maintain common target coverage requirements. Methods: Three patients were immobilized in full-body Vac-locs with a thermoplastic mask to cover the head and shoulders. Patients were initially aligned to the head and neck anatomy and to the pelvic girdle in upper body and lower body plans, respectively. Both plans were interrupted during delivery to determine the position of different anatomy (the T10 vertebrae in the upper body plan and the ankle region in the lower body plan) and to realign the patient. Results: Intrafraction motion in the S/I direction was within the uncertainty of the measurement. The positions of the same anatomy separated by up to 40 min correlate well (R>8) indicating that intrafraction motion is small. The standard deviations of the systematic setup errors were all under 2mm, and were marginally smaller in the upper body plan. The standard deviations of the random setup errors were twice as large in the L/R directions than in the A/P directions for both the upper and lower body plans. We determined the width of the blurred dose distribution penumbra specific to the interrupt locations. Conclusions: The resultant margins in the A/P directions and L/R directions were similar in both plans, under 8mm. The data presented only applies to the limited number of patients and interrupt positions chosen, and requires further investigation at many different anatomical sites in order to be applied over the whole body.
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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 | 0.000 | 0.001 |
| 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".