A framework for clinical commissioning of 3D‐printed patient support or immobilization devices in photon radiotherapy
Bibliographic record
Abstract
PURPOSE: The objective of this work is to outline a framework for dosimetric characterization that will comprehensively detail the clinical commissioning steps for 3D-printed materials applied as patient support or immobilization devices in photon radiotherapy. The complex nature of 3D-printed materials with application to patient-specific configurations requires careful consideration. The framework presented is generalizable to any 3D-printed object where the infill and shell combinations are unknown. METHODS: A representative cylinder and wedge were used as test objects to characterize devices that may be printed of unknown, patient-specific dimensions. A case study of a 3D-printed CSI immobilization board was presented as an example of an object of known, but adaptable dimensions and proprietary material composition. A series of measurements were performed to characterize the material's kV radiologic properties, MV attenuation measurements and calculations, energy spectrum water equivalency, and surface dose measurements. These measurements complement the recommendations of the AAPM's TG176 to characterize the additional complexity of 3D-printed materials for use in a clinical radiotherapy environment. RESULTS: The dosimetric characterization of 3D-printed test objects and a case study device informed the development of a step-by-step template that can easily be followed by clinicians to accurately and safely utilize 3D-printed materials as patient-specific support or immobilization devices. CONCLUSIONS: A series of steps is outlined to provide a formulaic approach to clinically commission 3D-printed materials that may possess varying material composition, infill patterns, and patient-specific dimensions.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".