Accuracy of Breath-hold CT in Treatment Planning for Lung Stereotactic Ablative Radiotherapy
Bibliographic record
Abstract
PURPOSE: The objectives of this study are (1) to measure concordance of tumor position on breath-hold (BH) computed tomography (CT) scans relative to the natural tumor path during free breathing (FB) and (2) to evaluate the benefits of the breathing monitoring device Abches (Apex Medical, Tokyo) for stereotactic ablative radiotherapy (SABR) treatment planning. METHODS: In 53 lung cancer patients treated with CyberKnife™ robotic radiosurgery system, FB four-dimensional computerized tomography (4DCT) and end-expiration (EE) BH CT images were obtained. Extent of natural tumor motion was assessed with rigid registration derived from end-inspiration (EI) and EE phases of the 4DCT. Tumor displacement in BH scans relative to the natural tumor path was measured relative to the EE 4DCT phase. RESULTS: Mean tumor motion (+/- 1 SD) during natural FB was 1 ± 1 mm, 2 ± 2 mm, and 6 ± 6 mm in medio-lateral, anterior-posterior, and cranio-caudal directions, respectively. Tumor position on BH CT scan was closer to EE than EI 4DCT phase for 35/53 patients (66%). Difference of BH tumor position vs. EE state was 4 ± 3 mm. Gross tumor displacements perpendicular to natural tumor path were as great as 11 mm (anterior-posterior) and were seen with or without the breathing monitoring device. CONCLUSION: Tumor position during BH CT may not accurately correspond to positions observed on FB 4DCT. Hence, accurate and custom 4D analysis for each individual patient is recommended for treatment planning, especially those involving BH acquisitions.
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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 | 0.013 |
| 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 | 0.001 | 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".