Assessing the impact of intrafraction motion correction on PTV margins and target and OAR dosimetry for single-fraction free-breathing lung stereotactic body radiation therapy
Bibliographic record
Abstract
The objective of this research is to investigate intrafraction motion correction on planning target volume (PTV) margin requirements and target and organ-at-risk (OAR) dosimetry in single-fraction lung stereotactic body radiation therapy (SBRT). Sixteen patients (15 with upper lobe lesions, 1 with a middle lobe lesion) were treated with single-fraction lung SBRT. Cone-beam computed tomography (CBCT) images were acquired before the treatment, between the arcs, and after the delivery of the treatment fraction. Shifts from the reference images were recorded in anterior-posterior (AP), superior-inferior (SI), and lateral (LAT) dimensions. The deviations from the reference image were calculated for 3 clinical scenarios: not applying intratreatment couch shifts and not correcting for pretreatment deviations < 3 mm ( scenario 1), not applying intratreatment couch shifts and correcting for pretreatment deviations < 3 mm ( scenario 2), and applying all pre- and intratreatment couch shifts (scenario 3). PTV margins were determined using the van Herk formalism for each scenario and maximum and average deviations were assessed. The clinical scenarios were modelled in the treatment planning system based on each patient dataset to assess target and OAR dosimetry. Calculated lower-bound PTV margins in the AP, SI, and LAT dimensions were [4.6, 3.5, 2.3] mm in scenario 1, [4.6, 2.4, 2.2] mm in scenario 2, and [1.7, 1.2, 1.0] mm in scenario 3. The margins are lower bounds because they do not include contributions from nonmotion related errors. Average and maximum intrafraction deviations were larger in the AP dimension compared to the SI and LAT dimensions for all scenarios. A unidimensional movement (several mm) in the negative AP dimension was observed in clinical scenarios 1 and 2 but not scenario 3. Average intrafraction deviation vectors were 1.2, 1.1, and 0.3 mm for scenarios 1, 2, and 3, respectively. Modelled clinical scenarios revealed that using scenario 3 yields significantly fewer treatment plan objective failures compared to scenarios 1 and 2 using a Wilcoxon signed-rank test. Intratreatment motion correction between each arc may enable reductions PTV margin requirements. It may also compensate for unidimensional negative AP movement, and improve target and OAR dosimetry.
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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 | 0.001 | 0.005 |
| 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".