14. Radiation Therapy Planning for Skin Cancer: Using 3D Surface Scanning to Localize Tumour
Bibliographic record
Abstract
Orthovoltage radiation therapy (ORT) is a non‑invasive treatment often used for patients with skin cancer, which is characterized by shallow tumours visible at the surface of the skin. Currently there is no commercially available treatment planning system for ORT. The first step of treatment planning is localizing the tumour in a computed tomography (CT) scan of the patient. We propose using 3D surface scanning to obtain a coloured and textured image of the patient, from which the tumour can be identified. The contour of the tumour can then be overlaid onto the CT image, for planning delivery of radiation therapy. This process was demonstrated using a male mannequin model, with a red sticker on the nose representing a skin tumour. A coloured and textured image of the face was obtained using a handheld 3D surface scanner [Figure 1]. The surface scan was aligned to a CT image of the mannequin head using a two‑step registration process, with a resulting error of 0.25mm. The tumour could then be easily segmented from the coloured surface scan by following the outline of the lesion. The tumour contour was extended in depth to 1cm, to encompass subdermal cancerous tissue in the treatment volume, and saved with the CT image for treatment planning [Figure 2]. This workflow is the first step to an open-source treatment planning system for ORT, which will allow physicians to deliver more precise treatment using ORT. This project was done in collaboration with the Kingston General Hospital.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".