Poster ‐ 48: Clinical assessment of ExacTrac stereoscopic imaging of spine alignment for lung SBRT
Bibliographic record
Abstract
Purpose: To evaluate the validity of using spine as a surrogate for tumor positioning with ExacTrac stereoscopic imaging in lung stereotactic body radiation therapy (SBRT). Methods: Using the Novalis ExacTrac x‐ray system, 39 lung SBRT patients (182 treatments) were aligned before treatment with 6 degrees (6D) of freedom couch (3 translations, 3 rotations) based on spine matching on stereoscopic images. The couch was shifted to treatment isocenter and pre‐treatment CBCT was performed based on a soft tissue match around tumor volume. The CBCT data were used to measure residual errors following ExacTrac alignment. The thresholds for re‐aligning the patients based on CBCT were 3mm shift or 3° rotation (in any 6D). In order to evaluate the effect of tumor location on residual errors, correlations between tumor distance from spine and individual residual errors were calculated. Results: Residual errors were up to 0.5±2.4mm. Using 3mm/3° thresholds, 80/182 (44%) of the treatments required re‐alignment based on CBCT soft tissue matching following ExacTrac spine alignment. Most mismatches were in sup‐inf, ant‐post, and roll directions which had larger standard deviations. No correlation was found between tumor distance from spine and individual residual errors. Conclusion: ExacTrac stereoscopic imaging offers a quick pre‐treatment patient alignment. However, bone matching based on spine is not reliable for aligning lung SBRT patients who require soft tissue image registration from CBCT. Spine can be a poor surrogate for lung SBRT patient alignment even for proximal tumor volumes.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".