Poster ‐ 46: Intra‐fraction tumor position assessment for lung SBRT in patients treated without customized immobilization devices
Bibliographic record
Abstract
Purpose: To assess intra‐fraction positional stability of lung cancer tumours in patients treated by kilo‐voltage cone‐beam computed tomography (CBCT)‐guided stereotactic body radiotherapy (SBRT) without the use of customized immobilization devices. Material and Methods: Twenty‐two patients underwent 4D‐CT in the supine position with the arms in a wing board but without customized immobilization. The PTV was the internal target volume based on maximum intensity projections and a 5mm symmetric setup margin. Treatments were planned using 7–9 static fields or two volumetric modulated arcs. At treatment, the patient position was adjusted using pre‐treatment CBCT. A post‐treatment CBCT was taken immediately after the treatment ended. The 41 CBCT pairs were automatically matched and the transitional shifts between the two CBCTs recorded. The mean values and standard deviations were calculated for these displacements. Results and conclusions: The mean time between CBCTs (treatment time) was 16.5 ± 6 minutes (range: 10 to 34 minutes). In all cases the tumour remained inside the PTV in the post‐treatment CBCT. The mean shifts between pre and post‐treatment CBCTs were −0.7 ± 1.6 mm (range −5.0 to 3.0 mm) vertically, −0.3 ± 1.7 mm (range −4.8 to 3.0 mm) longitudinally, and −0.4 ± 1.5 mm (range −4.0 to 2.0 mm) laterally. Our results suggest little systematic shifting during treatment, and standard deviations that are consistent with another published report for treatments where customized immobilization was used. This result is encouraging for SBRT programs in clinics with limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".