A process of efficient bolus shaping for cancer care
Bibliographic record
Abstract
In cancer therapy, tumorous cells are ruined without damaging to healthy tissues. External-Beam Radiotherapy is one of the most popular approaches for cancer treatment. Megavoltage photon beams from a linear accelerator are employed for rotating around a targeted area of patients from different angles to eradicate cancerous tissues. Bolus is a sheet of material with a uniform thickness (usually 10 mm) to cover the skin surface to minimize damage to healthy tissues while keeping the desired dose. The use of the bolus can increase the surface dose and improve the dosage distribution. The existing clinical method of shaping the bolus to cover patient's surface is a manual process based on trial and error. A significant problem of the existing method is air gaps that are generated between the bolus and the patient's skin, which results in the lower distribution of dosage. 3D printing is also an approach to form the bolus, but this process is time-consuming and limited to certain types of materials. The objective of this research is to develop a process for bolus shaping to reduce air gaps and improve efficiency of the bolus fabrication. 3D scanned data of the target surface are simplified with different methods to find the best approach to fit the need of this work. Different tools are used to generate 2D shape patterns from the simplified 3D models. The generated 2D patterns and features of the tools are studied. The 2D patterns are cut using a laser cutting machine and folded back to the 3D bolus shape. Fabricated 3D bolus models are evaluated by comparing them with original design shapes, which shows the satisfaction to meet requirements of the bolus application.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".