A commercial treatment planning system with a hybrid dose calculation algorithm for synchrotron radiotherapy trials
Bibliographic record
Abstract
Abstract Synchrotron Radiotherapy (SyncRT) is a preclinical radiation treatment which delivers synchrotron x-rays to cancer targets. SyncRT allows for novel treatments such as Microbeam Radiotherapy, which has been shown to have exceptional healthy tissue sparing capabilities while maintaining good tumour control. Veterinary trials in SyncRT are anticipated to take place in the near future at the Australian Synchrotron's Imaging and Medical Beamline (IMBL). However, before veterinary trials can commence, a computerised treatment planning system (TPS) is required, which can quickly and accurately calculate the synchrotron x-ray dose through patient CT images. Furthermore, SyncRT TPS's must be familiar and intuitive to radiotherapy planners in order to alleviate necessary training and reduce user error. We have paired an accurate and fast Monte Carlo (MC) based SyncRT dose calculation algorithm with Eclipse TM , the most widely implemented commercial TPS in the clinic. Using Eclipse TM , we have performed preliminary SyncRT trials on dog cadavers at the IMBL, and verified calculated doses against dosimetric measurement to within 5% for heterogeneous tissue-equivalent phantoms. We have also performed a validation of the TPS against a full MC simulation for constructed heterogeneous phantoms in Eclipse TM , and showed good agreement for a range of water-like tissues to within 5%–8%. Our custom Eclipse TM TPS for SyncRT is ready to perform live veterinary trials at the IMBL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".