Sci-Thur AM: Planning - 06: Planning target volume margin suitability in lung stereotactic body radiation therapy: A preliminary evaluation using cone-beam computed tomography
Bibliographic record
Abstract
Stereotactic body radiation therapy (SBRT) requires precise delivery of radiation to the target; intra- and inter-fraction lung tumour motion may adversely impact local tumour control. The purpose of this study was to retrospectively evaluate the impact of planning target volume (PTV) margin size on the coverage of the internal target volume (ITV) as localized in pre- and post-treatment cone-beam computed tomography (CBCT) images. Data from two patients undergoing SBRT were evaluated. For planning, free-breathing and 4DCT scans were performed, and used to contour the ITV. A 5mm margin was added to create the PTV. During treatment, 14 CBCTs were collected pre- and post-beam delivery. A data set comprising the average 4DCT intensities where available and treatment planning CT intensities for voxels that were beyond the field of view of the 4DCT was constructed. Registration of the combined planning image to each CBCT was performed using a deformable image registration algorithm. The transformations aligning the combined planning image with the CBCTs were applied to the planning ITV to obtain the treatment ITVs. For each CBCT, the fraction of treatment ITV within the PTV was determined using Boolean logic. This was repeated for various PTV margins ranging from 0 to 10 mm at 1mm intervals. The 3 and 5 mm PTV margins covered 95.1 ± 5.9% and 99.0 ± 2.0% of the ITV, respectively. Analysis of additional patients will be performed to confirm these preliminary results, which reinforce the use of a 5mm PTV margin for lung SBRT.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".