Influence of intra- and interfraction motion on planning target volume margin in liver stereotactic body radiation therapy using breath hold
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate intra- and interfraction motion during liver stereotactic body radiation therapy for the purpose of planning target volume (PTV) margin estimation, comparing deep inspiration breath hold (DIBH) and deep expiration breath hold (DEBH). METHODS AND MATERIALS: Pre- and posttreatment kV cone beam computed tomography (CT) images were acquired for patients with liver cancer who were treated using stereotactic body radiation therapy with DIBH or DEBH. A total of 188 images were analyzed from 18 patients. Positioning errors were determined based on a comparison with planning CT images and matching to the liver. Treatment did not proceed until errors were ≤3 mm. Standard deviations of random and systematic errors resulting from this image matching process were used to calculate PTV margin estimates. RESULTS: DIBH errors are generally larger than DEBH errors, especially in the anterior-posterior and superior-inferior directions. Posttreatment errors tend to be larger than pretreatment errors, especially for DIBH. Standard deviations of random errors are larger than those of systematic errors. Considering both pre- and posttreatment cone beam CT images, PTV margins for DIBH and DEBH are estimated as anterior-posterior, superior-inferior, right-left = (5.7, 6.3, 3.0) mm and (3.1, 3.4, 2.8) mm, respectively. CONCLUSIONS: This study suggests that DEBH results in more reproducible target positioning, which could in turn justify the use of smaller PTV margins.
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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 | 0.002 |
| 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) | 0.000 | 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".