SU‐F‐BRB‐01: How Effective Is Abdominal Compression at Reducing Lung Motion? An Analysis Using Deformable Image Registration Within Different Sub‐Regions of the Lung
Bibliographic record
Abstract
Purpose: To investigate the effectiveness of employing abdominal compression (AC) in reducing motion for the target region and sub‐regions of the lung as part of the planning process for radiation therapy. Methods: Fourteen patients with early lung cancer were scanned with 4DCT and it was determined that target motion exceeded our institutional limit of > 8 mm motion and received a repeat 4DCT with AC. For each 4DCT, deformable image registration (DIR) was used to map the max inhale to the max exhale phase to determine the deformation vector fields (DVF). DIR was performed with Morphons and Demons algorithms. The mean DVF was used to represent that sub‐region for each patient. The magnitudes of the mean DVF were quantified for the target and 12 sub‐regions in the AP, LR SI directions. The sub‐regions were contoured on each lung as (add prefix R or L for lung): Upper‐Anterior (UA), Upper‐Posterior (UP), Mid‐Anterior (MA), Mid‐Posterior (MP), Lower‐Anterior (LA) and Lower‐Posterior (LP). Results: The min/max SI motion for the target on the uncompressed 4DCT was 8mm/24.5 mm. The magnitude of decrease in SI was greatest in the RLP region (3.7±4.0mm) followed by target region (3.3±2.2mm) and finally the LLP region (3.0±3.5mm). The magnitude of decrease in 3D vector followed the same trend; RLP (3.5±2.2mm) then GTV (3.5±2.6mm) then LLP (2.7±3.8mm). 79% of the cases had a SI decrease of >12.5%, 43% had a SI decrease of >25% and 21% had a SI decrease of >50% as compared to the motion on the uncompressed 4DCT. Conclusion: AC is useful in reducing motion with the largest decreases observed in the lower posterior regions of the lungs. However, it should be noted that AC will not greatly decrease motion for all cases as 21% of cases did not reduce SI motion more than 12.5% of initial motion.
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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 | 0.003 |
| 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) | 0.001 | 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".