Poster – 39: Using Optical Scanner and 3D Printer Technology to Create Lead Shielding for Radiotherapy of Facial Skin Cancer with Low Energy Photons
Bibliographic record
Abstract
Purpose: Treatment of skin cancers of the face using orthovoltage radiotherapy often requires lead shielding. However, creating a lead shield can be difficult because the face has complex and intricate contours. The traditional process involved creating a plaster mould of the patient's face can be difficult for patients. Our goal was to develop an improved process by using an optical scanner and 3D printer technology. Methods: The oncologist defined the treatment field by drawing on each patient's skin. Three‐dimensional images were acquired using a consumer‐grade optical scanner. A 3D model of each patient's face was processed with mesh editing software before being printed on a 3D printer. Using a hammer, a 3 mm thick layer of lead was formed to closely fit the contours of the model. A hole was then cut out to define the field. Results: The lead shields created were remarkably accurate and fit the contours of the patients. The hole defining the field exposed only a minimally sized site to be exposed to radiation, while the rest of the face was protected. It was easy to obtain perfect symmetry for the definition of parallel opposed beams. Conclusion: We are routinely using this technique to build lead shielding that wraps around the patient as an alternative to cut‐outs. We also use it for treatment of the tip of the nose using a parallel opposed pair beams with a wax nose block. We found this technique allows more accurate delineation of the cut‐out and a more reproducible set‐up.
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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) | 0.000 | 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 | 0.000 |
| 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".