Tumor Shape‐Specific Brachytherapy Implants by 3D‐Printing, Precision Radioactivity Painting, and Biomedical Imaging
Bibliographic record
Abstract
In brachytherapy (BT), or internal radiation therapy, cancer is treated by radioactive implants. For instance, episcleral plaques (EPs) for the treatment of uveal melanoma, are designed according to generic population approximations. However, more personalized implants can enhance treatment precision through better adjustment of dose profiles to the contours of cancerous tissues. An original approach integrating biomedical imaging, 3D printing, radioactivity painting, and biomedical imaging, is developed as a workflow for the development of tumor shape-specific BT implants. First, computer-aided design plans of EP are prepared according to guidelines prescribed by the Collaborative Ocular Melanoma Study protocol. Polyetheretherketone (PEEK), a high-performance polymer suitable for permanent implants, is used to 3D-print plaques and the geometrical accuracy of the printed design is evaluated by imaging. The possibility to modulate the dose distribution in a tridimensional manner is demonstrated by painting the inner surfaces of the EPs with radioactive 103Pd, followed by dose profile measurements. The possibility to modulate dose distributions generated by these 3D-printed plaques through radioactivity painting is therefore confirmed. Ex vivo surgical tests on human eyeballs are performed as an assessment of manipulation ease. Overall, this work provides a solution for the fabrication of tumor-specific radioactive implants requiring higher dose precision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".