Sci—Thur AM: YIS ‐ 07: Design and production of 3D printed bolus for electron radiation therapy
Bibliographic record
Abstract
This is a proof‐of‐concept study demonstrating the capacity for modulated electron radiation therapy (MERT) using 3D printed bolus. Previous reports have involved bolus design using an electron pencil beam model and fabrication using a milling machine. In this study, an in‐house algorithm is presented that optimizes the dose distribution with regard to dose coverage, conformity and homogeneity within planning target volume (PTV). The algorithm uses calculated result of a commercial electron Monte Carlo dose calculation as input. Distances along ray lines from distal side of 90% isodose to distal surface of PTV are used to estimate the bolus thickness. Inhomogeneities within the calculation volume are accounted for using coefficient of equivalent thickness method. Several regional modulation operators are applied to improve dose coverage and uniformity. The process is iterated (usually twice) until an acceptable MERT plan is realized, and the final bolus is printed using solid polylactic acid. The method is evaluated with regular geometric phantoms, anthropomorphic phantoms and a clinical rhabdomyosarcoma pediatric case. In all cases the dose conformity is improved compared to that with uniform bolus. The printed boluses conform well to the surface of complex anthropomorphic phantoms. For the rhabdomyosarcoma patient, the MERT plan yields a reduction of mean dose by 38.2% in left kidney relative to uniform bolus. MERT using 3D printed bolus appears to be a practical, low cost approach to generating optimized bolus for electron therapy. The method is effective in improving conformity of prescription isodose surface and in sparing immediately adjacent normal tissues.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".