Development and validation of a treatment planning system for small animal radiotherapy: SmART-Plan
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Image-guided equipment for precision irradiation of small animals for pre-clinical radiotherapy research became recently available. To enable downscaled radiotherapy studies that can be translated into human radiotherapy knowledge, a treatment planning system for pre-clinical studies is required. MATERIAL AND METHODS: A dedicated treatment planning system (SmART-Plan) for small animal radiotherapy studies was developed. It is based on Monte Carlo simulation of particle transport in an animal. The voxel geometry is derived from the onboard cone beam CT imaging panel. SmART-Plan was validated using radiochromic film (RCF) dosimetry in various phantoms: uniform, multislab and a realistic plasticized mouse geometry. RESULTS: Good agreement was obtained between SmART-Plan dose calculations and RCF dose measurements in all phantoms. For various delivered plans agreement was obtained within 10% for the majority of the targeted dose region, with larger differences between 10% and 20% near the penumbra regions and for the smallest 1mm collimator. Absolute depth and lateral dose distributions showed better agreement for 5 and 15-mm collimators than for a 1-mm collimator, indicating that accurate dose prediction for the smallest field sizes is difficult. CONCLUSION: SmART-Plan offers a useful dose calculation tool for pre-clinical small animal irradiation studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".