A retrospective study on clinical factors influencing intra-fraction motion using volumetric imaging for spine stereotactic body radiotherapy.
Bibliographic record
Abstract
Objectives: Stereotactic body radiation therapy (SBRT) for the spine is challenging due to high-dose gradients sparing the cord in the treatment plans. We present our findings of initial setup error and intrafraction motion from Cone-beam computed tomography (CBCT) imaging. Materials and methods: A total of 47 patients treated with spine SBRT with a total of 154 fractions following a fractionation schedule of 16 Gy in 1, 24 Gy in 2, and 30 Gy in 5 fractions were part of this study. Pre-treatment CBCT was used for localization of the target and couch shifts were applied based on target volume matching to the planning CT image set. Post-treatment CBCT was acquired for all fractions. Intrafraction motion (IFM) was calculated by matching post-treatment CBCT to planning CT for the target volume. Results: The average Intrafraction motion was 1.6 ± 0.9 mm for the study cohort. The average and standard deviation of intrafraction motion were 0.4 ± 1.1 (AP), 0.3 ± 0.9 (SI) and 0.2 ± 1.2 (RL) respectively. The average Initial setup error tabulated from the offline review showed a mean value of 7.8 ± 5.3 mm. The average and standard deviation of the initial setup error were 2.5 ± 5.5 (AP), 2.4 ± 5.3(SI), and 0.8 ± 4.5(RL) respectively. The correlation of intrafraction motion with body mass index (BMI) and the number of consecutive vertebrae levels did not show any statistical significance, however, there was a significant association with gender as women showed more IFM. Conclusions: Our study on intrafraction motion from CBCT images reinforced the importance of immobilization and imaging for positioning spine SBRT patients. Advances in knowledge: The need for CBCT and imagining for positional errors is emphasized while treating with SBRT spine and the need for proper immobilization techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".