TH-A-137-08: On the Validity of Target Density Overrides for RapidArc Lung SBRT Treatment Planning
Bibliographic record
Abstract
Purpose: It is common practice to calculate lung SBRT plans on free breathing CT scans. Recently, literature has suggested using time average scans or ITV density overridden scans to more accurately calculate dose in lung. We evaluate each of these techniques. Methods: A free breathing (FB) and 4D-CT were acquired for a Quasar Motion Phantom with lung insert and 3 cm spherical target (Modus Medical Devices Inc, Canada). Target motion was set to ± 1cm parallel to CT table travel at 16 cycles per minute. An ITV was defined on the 4D-CT with a PTV defined as a 5 mm expansion. RapidArc plans giving 1000 cGy to 96–97% of the PTV in two arcs were created on four image sets: the time average, FB, FB with the ITV set to tissue density, and FB with the PTV set to tissue density. Plans were delivered on a Varian TrueBeam STx (Varian Medical Systems, Palo Alto, CA) and measured on EBT3 film placed in the phantom target. Reference dose distributions that accounted for film motion were created for each plan by shifting the dose in 2 mm steps over the range of motion and convolving these shifted doses. The film was analyzed in with a gamma metric of 1mm/1%. Results: The passing gamma percentages were 77.9%, 83.6%, 89.1%, and 92.7% for the FB, time average, ITV, and PTV plans, respectively. Profiles along the direction of travel showed hot spots of 5-10% above the predicted dose just outside the target for the FB and average plans. The ITV plan showed similar hot spots ∼2% above predicted dose. The PTV profile was in good agreement with predicted dose. Conclusion: Though this study is a greatly simplified case, it shows that planning to a tissue density target in lung may produce the most accurate dose predictions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".