Enhancing patient positioning accuracy: evaluating daily cone beam computed tomography in the halcyon system
Bibliographic record
Abstract
BACKGROUND: Precise patient positioning is crucial for successful radiotherapy, ensuring accurate delivery of radiation to tumors while minimizing exposure to healthy tissues. Positional errors can significantly impact treatment efficacy and increase side effects. This study evaluates the effectiveness of daily cone beam computed tomography (CBCT) imaging in the Halcyon system for detecting and correcting patient misalignments across various cancer types and treatment sites. METHODS: A retrospective analysis was conducted on 411 patients treated with the Varian Halcyon linear accelerator from August 2022 to August 2024. Patients were grouped based on tumor location: Head and Neck (118 patients), Chest (188 patients), and Pelvis (105 patients). Daily pre-treatment CBCT scans were performed to verify positioning, with shifts in the x, y, and z axes quantified and adjusted using automated couch corrections. RESULTS: The study revealed average positional shifts along the x-axis of ~0.112 cm, while both the Chest and Pelvic groups recorded 0.194 cm. The y-axis deviations were 0.135 cm for Head and Neck, 0.206 cm for Chest, and 0.195 cm for the Pelvis. On the z-axis, a mean deviation of 0.07 cm was found for the Head and Neck group, while 0.11 cm for the Chest group, and 0.085 cm for the Pelvic group. The Head and Neck group exhibited the smallest standard deviations across all axes, indicating greater positional consistency. Normalized density distributions showed distinct emergent patterns, the Head and Neck group showing tighter distributions compared to the broader distributions observed in the Chest and Pelvic groups. CONCLUSIONS: Daily CBCT imaging in the Halcyon system significantly enhances patient positioning accuracy in radiotherapy. The findings demonstrate that this approach minimizes positional shifts, particularly in the Head and Neck region, essential for optimizing treatment outcomes and reducing the risk of adverse effects. Future studies should further explore the integration of advanced imaging techniques to improve precision in patient positioning.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".