Evaluation of the Efficacy of Rotational Corrections for Standard-Fractionation Head and Neck Image-Guided Radiotherapy
Bibliographic record
Abstract
PURPOSE: Modern linear accelerators are equipped with cone beam computed tomography and robotic couches that can correct for errors in the translational (X, Y, Z) and rotational (α, β, γ) axes prior to treatment delivery. Here, we compared the positional accuracy of 2 cone beam registration approaches: (1) employing translational shifts only in 3 degrees of freedom (X, Y, Z), versus; (2) using translational-rotational shifts in 6 degrees of freedom (X, Y, Z, α, β, γ). METHODS: This retrospective study examined 140 interfraction cone beam images from 20 patients with head and neck cancer treated with standard intensity-modulated radiation therapy. The cone beam images were matched to planning simulation scans in 3, then in 6 degrees of freedom, using the mandible, clivus, and C2 and C7 vertebrae as surrogate volumes. Statistical analyses included a generalized mixed model and was used to assess whether there were significant differences in acceptable registrations between the 2 correction methods. RESULTS: The rates of improvement with corrections in 6 degrees of freedom for the mandible with a 5-mm expansion margin were 54.55% ( P = .793), for the clivus 85.71% ( P = .222), and for C7 87.50% ( P = .015). There was a 100% increase in acceptability for the C2 vertebra within the 5-mm margin ( P < .001). For the 3-mm expansion margin, the rates of improvement for the mandible, clivus, C2, and C7 were 63.16% ( P = .070), 91.30% ( P = .011), 84.21% ( P = .027), and 76.92% ( P < .001), respectively. CONCLUSIONS: Significant registration improvements with the use of rotational corrections with a 5-mm expansion margin are only seen in the C7 vertebra. At the 3-mm margin, significant improvements are found for the C2, C7, and clivus registrations, suggesting that intensity-modulated radiotherapy treatments for head and neck cancers with 3-mm planning target volume margins may benefit from corrections in 6 degrees of freedom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".