Radiotherapy Immobilization Mask Molding Through the Use of 3D-Printed Head Models
Bibliographic record
Abstract
Purpose: To evaluate the feasibility of a workflow free of a simulation appointment using three-dimensional-printed heads and custom immobilization devices. Materials and Methods: Simulation computed tomography scans of 11 patients who received radiotherapy for brain tumors were used to create three-dimensional printable models of the patients’ heads and neck rests. The models were three-dimensional-printed using fused deposition modeling and reassembled. Then, thermoplastic immobilization masks were molded onto them. These setups were then computed tomography-scanned and compared against the volumes from the original patient computed tomography-scans. Following translational +/− rotational coregistrations of the volumes from three-dimensional-printed models and the patients, the similarities and accuracies of the setups were evaluated using Dice similarity coefficients, Hausdorff distances, differences in centroid positions, and angular deviations. Potential dosimetric differences secondary to inaccuracies in the rotational positioning of patients were calculated. Results: Mean angular deviation of the 3D-printout from the original volume for the Pitch, Yaw, and Roll were 1.1° (standard deviation = 0.77°), 0.59° (standard deviation = 0.41°), and 0.79° (standard deviation = 0.86°), respectively. Following translational + rotational shifts, the mean Dice similarity coefficients of the three-dimensional-printed and original volumes was 0.985 (standard deviation = 0.002) while the mean Hausdorff distance was 0.9 mm (standard error of the mean: 0.1 mm). The mean centroid vector displacement was 0.5 mm (standard deviation: 0.3 mm). Compared to plans that were coregistered using translational + rotational shifts, the D 95 of the brain from three-dimensional-printed heads adjusted for TR shifts only differed by −0.1% (standard deviation = 0.2%). Conclusions: Patient head volumes and positions at simulation computed tomography scans can be accurately reproduced using three-dimensional-printed models, which can be used to mold radiotherapy immobilization masks onto. This strategy, if applied on diagnostic computed tomography scans, may allow symptomatic and frail patients to avoid a computed tomography-simulation and mask molding session in preparation for palliative whole brain radiotherapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".