Poster — Thur Eve — 35: Lung SBRT: 4DCT Based Treatment Planning in Presence of Respiratory Motion
Bibliographic record
Abstract
The purpose of this study was to determine the impact of internal target volume (ITV) density on the 4DCT based lung stereotactic body radiotherapy (SBRT) treatment planning (TP) using the ITV density assignment correction (DAC). Siemens 40‐slice CT scanner was used to acquire 8 phases of 4DCT images and a free breathing CT scan of CIRS Dynamic Thorax Phantom with the tumor moving in three dimensions. The ITV was created from the merge of all the CTVs in all the phases in Eclipse and copied to the free breathing CT scan for planning dose calculation. The PTV was created by adding a 5mm margin around the ITV. Lung SBRT plans were created using the 0% inhale phase, the 50% inhale phase and the free breathing CT scan under the original CT scan and DAC in the ITV. Our data shows ITV coverage from the isodose line, DVH analysis and mean dose on the free breathing CT scan, 0% inhale phase and 50% inhale phase with and without ITV DAC. The high dose region follows the CTV without DAC in the ITV because of the higher build‐up of dose within the denser CTV volume than in the surrounding less dense lung tissue. The MU decreased 0.9%, 1.3% and 1.2% for free breathing scan, 0% inhale and 50% inhale respectively with DAC in the ITV. The DAC provided a clinical acceptable way to predict the ITV dose coverage in the treatment planning 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".