Evaluation of positioning accuracy in head-and-neck cancer treatment: A cone beam computed tomography assessment of three immobilization devices with volumetric modulated arc therapy.
Bibliographic record
Abstract
In this study, we assessed the precision and repeatability of the daily patient positioning for three distinct immobilization devices used for head-and-neck patients undergoing RapidArc radiation therapy using cone beam computed tomography (CBCT). An analysis was conducted on the accuracy of patient setup for three distinct immobilization devices, resulting in 1204 CBCT images for 189 patients in total. Using a typical posifix supine headrest and five fixation point podcast-plus-thermoplastic masks, the first group of 39 patients (125 CBCTs) was immobilized. The identical method was used to immobilize the second group of 19 patients (158 CBCTs) in the same posture (supine), and AccuFormTM custom headrests were employed as an added measure. Over 65% of the patients in the third group had a double shell positioning system (DSPS) covering their entire head and neck. Patient-alignment-accuracy or couch shifts in the vertical, longitudinal, and lateral directions from CT-CBCT fusions were recorded from ARIA. Our results showed that in 90% of the anteriorposterior (AP), 90% of the superior-inferior (SI), and 92.7% of the right-left (RL) population in the first group, patient-alignment-accuracy or couch shifts were within 2 mm. For 99.4% (AP), 100% (SI), and 98.7% (RL) of the second group's total population, patient-alignment-accuracy was within 2 mm. In the third group, it was within 2 mm for 92.1% (AP), ~89% (SI), and 93.3% (RL) of the total population. In conclusion, a significant improvement was seen with the application of a mouth bite and a tailored backrest cushion to the five fixation point posicast mask. In addition, significant improvement in the alignment of the lower neck area was observed with the use of DSPS. Virtually 100% of the head-and-neck patients were aligned within an accuracy of 3 mm, which is the PTV margin in our department.
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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 | 0.005 |
| 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 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".