Evaluating Accufix Head and Neck Shoulder Immobilization for Head and Neck Radiation Therapy
Bibliographic record
Abstract
The field of radiation therapy has seen significant advancements in treatment precision, such as proton therapy and intensity-modulated radiotherapy, often involving various immobilization devices for patient positioning and motion monitoring. However, the effectiveness of shoulder immobilization systems, particularly in the neck and shoulder regions, requires further investigation due to conflicting results and limited studies. This study aims to evaluate the accuracy and effectiveness of the Accufix™ Head and Neck Device shoulder cantilever depression system in reducing interfractional and intrafractional movement in head and neck cancer (HNC) patients. This device positions the head, neck, and shoulders, lowering the shoulders to precisely target head and neck tumors with radiation beams. Patient data was collected for 3 larynx cancer patients and 1 tongue cancer patient undergoing volumetric-modulated arc therapy (VMAT) radiation therapy at the Juravinski Cancer Centre. Patients were immobilized using a head and neck thermoplastic mask and an Accufix™ Head and Neck device. AlignRT, an optical surface monitoring system (OSMS), was utilized to track real-time body surface movements during treatment in 3 translation directions (AP: anterior-posterior, SI: superior-inferior, and LR: left-right) and 3 rotations (pitch, roll, and yaw). Interfractional shoulder positioning discrepancies were evaluated by conducting cone-beam computed tomography (CBCT) scan and planning computed tomography (CT) scan image registration in two anatomical locations: the target volume (T-IM) and the mid-clavicles (C-IM). Intrafractional motion across all patients remained low for both translational and rotational shifts, with 6.42% exceeding a 5mm margin and 0.02% exceeding a 3º margin, respectively. Differences between target TV-IM and C-IM remained within +/- 3mm of shifts for the majority of fractions. Little consistency was found between AlignRT data and C-IM data, with shifts ranging from 10mm to -5mm, attributed to the surface geometry and shape of the region of interest (ROI) we tracked. While shoulder immobilization using the Accufix™ system was found to be sufficient, AlignRT's accuracy in reproducing patient shoulder positioning was limited in our study. Factors influencing surface-guided systems, such as ROI size and location, need careful evaluation. Further studies on SGRT use compared to immobilization devices are needed to validate these findings and explore potential improvements.
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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 | 0.004 |
| 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) | 0.001 | 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".