3D-printed radiopaque episcleral plaques with radioactive collimating cavities for enhanced dose delivery in brachytherapy
Bibliographic record
Abstract
PURPOSE Episcleral plaque brachytherapy (EPBT) is a well-established treatment. However, the lateral dose to healthy tissues, such as the sclera, retina, and optic nerve is often problematic and results in side effects. This study proposes an innovative approach based on the 3D-printing of radiopaque polymer plaques featuring cylindrical radioactive cavities (CRC) with a potential collimating effect on radiation delivery to tumors. METHODS AND MATERIALS A CAD model based on the COMS protocol was created and 3D-printed using radiopaque PEEK polymer. Cylindrical cavities (1 mm depth/diameter) were evenly spaced on the plaque's inner surface. Two radioactive layouts (RL 1 : uniform loading; RL 2 : radial gradient loading) were designed. µCT imaging was used to assess the geometric accuracy of the 3D-printed CRC EPs, and dose distribution was evaluated for the two (2) radioactive layouts using MAGIC-pf gel dosimetry and T 2 -weighted MRI. The resulting dose profiles were compared with those generated by both COMS and SEP plaques. RESULTS Radiopaque CRC EPs showed higher central axis dose deposition while minimizing lateral overexposure compared to COMS and SEP plaques, while also providing robust back-shielding. Dose profiles from RL 1 CRC EPs (uniform layout) extended deeper into the eye, whereas RL 2 CRC EPs (with gradient) exhibited a more rapid dose fall-off, producing a concentrated, spherical dose distribution. CONCLUSIONS 3D-printed radiopaque EPs with radioactivity encapsulated in cylindrical cavities demonstrated the ability to achieve more forward-projected dose profiles in EPBT. This fabrication design and a modulated radioactivity distribution across the EP surface would enable more precise and deeper dose delivery while reducing radiation exposure to lateral healthy tissues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".