Characterization of novel 3D‐printed metal shielding for brachytherapy applicators
Bibliographic record
Abstract
PURPOSE: To characterize 3D-printed stainless steel metal samples in the presence of an Iridium-192 source for organ-at-risk sparing in gynecologic brachytherapy. METHODS: , thickness range 1-5 mm) were embedded in a solid water phantom at varying distances from source catheters. An Ir-192 brachytherapy source was passed through the phantom and the dose was measured using EBT3 Gafchromic film. The film was initially positioned in the sagittal plane 2 cm away from the catheters, with the metal directly below and then 1 cm from the film. A uniform dose was delivered at the film plane. A second setup measured a depth dose curve in solid water with film in the transverse plane directly above the metal samples. This setup was recreated using Monte Carlo simulations (EGSnrc egs_brachy). Validation between methods was performed with unshielded (solid water only) measurements. RESULTS: The planar dose passing through the metal samples (thickness 1-5 mm) at the midpoint between the film and catheters, decreased compared to solid water by 7.4% ± 6.9% to 26.5% ± 5.5%. Dose enhancement on the order of 5% was noted when metal was directly adjacent to the film. The average decrease in depth dose from a single dwell position ranged from 10.0% ± 5.9% (1 mm) to 21.1% ± 5.3% (5 mm) as measured with film, and from 3.8% ± 0.9% (1 mm) to 16.3% ± 0.9% (5 mm) using MC simulation. The average depth dose values were measured using a line width of 2.5 mm for film, and 3 mm for MC simulation, and the measurements generally agree within standard error. CONCLUSIONS: The 3D-printed metal samples show potential for personalized applicators. Maximum dose reduction of 26.5% ± 5.5% compared to solid water was measured at 2 cm from the source using the 5 mm sample. An outer layer of solid water could potentially be used to reduce dose enhancement due to increased scatter near the metal.
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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 | 0.001 | 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".