Poster ‐ 40: Treatment Verification of a 3D‐printed Eye Phantom for Proton Therapy
Bibliographic record
Abstract
Purpose: Ocular melanoma is a form of eye cancer which is often treated using proton therapy. The benefit of the steep proton dose gradient can only be leveraged for accurate patient eye alignment. A treatment‐planning program was written to plan on a 3D‐printed anatomical eye‐phantom, which was then irradiated to demonstrate the feasibility of verifying in vivo dosimetry for proton therapy using PET imaging. Methods: A 3D CAD eye model with critical organs was designed and voxelized into the Monte‐Carlo transport code FLUKA. Proton dose and PET isotope production were simulated for a treatment plan of a test tumour, generated by a 2D treatment‐planning program developed using NumPy and proton range tables. Next, a plastic eye‐phantom was 3D‐printed from the CAD model, irradiated at the TRIUMF Proton Therapy facility, and imaged using a PET scanner. Results: The treatment‐planning program prediction of the range setting and modulator wheel was verified in FLUKA to treat the tumour with at least 90% dose coverage for both tissue and plastic. An axial isotope distribution of the PET isotopes was simulated in FLUKA and converted to PET scan counts. Meanwhile, the 3D‐printed eye‐phantom successfully yielded a PET signal. Conclusions: The 2D treatment‐planning program can predict required parameters to sufficiently treat an eye tumour, which was experimentally verified using commercial 3D‐printing hardware to manufacture eye‐phantoms. Comparison between the simulated and measured PET isotope distribution could provide a more realistic test of eye alignment, and a variation of the method using radiographic film is being developed.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".