Range verification in heavy-ion therapy using a hadron tumour marker
Bibliographic record
Abstract
Abstract Objective. A new method to estimate the range of an ion beam in a patient during heavy-ion therapy was investigated, which was previously verified for application in proton therapy. Approach. The method consists of placing a hadron tumour marker (HTM) close to the tumour. As the treatment beam impinges on the HTM, the marker undergoes nuclear reactions. When the HTM material is carefully chosen, the activation results in the emission of several delayed, characteristic γ rays, whose intensities are correlated with the remaining range inside the patient. When not just one but two reaction channels are investigated, the ratio between these two γ ray emissions can be measured, and the ratio is independent of any beam delivery uncertainties. Main results. A proof-of-principle experiment with an 16 O ion beam and Ag foils as HTM was successfully executed. The 107 Ag( 16 O, x ) 112 Sb and the 107 Ag( 16 O, x ) 114 Sb reaction channels were identified as suitable for the HTM technique. When only one γ -ray emission is measured, the resulting range-uncertainty estimation is at the 0.5 mm scale. When both channels are considered, a theoretical limit on the range uncertainty of a clinical fiducal marker was found to be ±290 μ m. Significance. Range uncertainty of a heavy-ion beam limits the prescribed treatment plan for cancer patients, especially the direction of the ion beam in relation to any organ at risk. An easy to implement range-verification technique which can be utilized during clinical treatment would allow treatment plans to take full advantage of the sharp fall-off of the Bragg peak without the risk of depositing excessive dose into healthy tissue.
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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".