Improved sub-milimeter range-verification method for proton therapy using a composite hadron tumour marker (HTM)
Bibliographic record
Abstract
Abstract Objective. The results of a follow-up experiment investigating a novel method for sub-milimetre range verification (RV) in proton therapy (PT) are presented. Approach. The method consists of implanting a hadron tumour marker (HTM) near the planned treatment volume, and measuring the γ-ray signals emitted as a result of activation by the proton beam. These signals are highly correlated with the energy of the beam impinging on the HTM and can provide an absolute measurement of the range of the beam relative to the position of the HTM, which is independent of any uncertainties in beam delivery. Main results. Three candidate HTM materials were identified and combined into a single composite HTM, which makes use of the strongest reaction in each material. The setup of the previous experiment was improved on by using high-purity germanium detectors to measure the γ-ray signal with a higher resolution than was previously achieved. A PMMA phantom was also used to simulate the γ-ray background from tissue activation. HTM RV using the data collected in this study yielded range measurements whose average deviation from the expected value was 0.13(22)mm. Significance. Range uncertainty in PT limits the prescribed treatment plan for cancer patients with large safety margins and constrains the direction of the proton beam in relation to any organ at risk. The sub-milimetre range uncertainty achieved in this study using HTM RV, if implemented clinically, would allow for a reduction in the size of safety margins, increasing the therapeutic window for PT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".