A finite volume approach to dosimetric calculations in diffusing alpha-emitters radiation therapy
Bibliographic record
Abstract
Abstract Objective. In diffusing alpha-emitters radiation therapy (‘Alpha DaRT’), the diffusion-leakage (DL) model is used to determine the spatial distributions of the emitters and the corresponding alpha dose, critical for a successful treatment. This work first introduces a finite volume (FV) approach to develop numerical schemes to simulate the DL model in one, two and three dimensions then presents how variations over realistic ranges of the DL model parameters related to desorption, diffusion and leakage processes affect the alpha dose distribution and the position of the clinically significant alpha particle 10 Gy isodose. This work also presents the effects of three modeling approximations: two source geometry approximations (solid cylinder instead of hollow, pixelized cross section instead of circular), and one dosimetric approximation (single-source dose superposition instead of multiple-sources direct dose calculation). Approach. The introduced FV approach was used to obtain spatial distributions of the emitters, from which the corresponding alpha dose distributions were calculated under the assumption of a local deposition of the alpha particles’ energies. Variation ranges of the DL model parameters were based on previously published data. For each modeling approximation studied, the error and relative error on the alpha dose distribution were calculated and the displacement of the 10 Gy isodose was evaluated. Main results. Over realistic ranges, the desorption probabilities, diffusion lengths, and leakage probabilities affect the position of the alpha particle 10 Gy isodose by ∼ 0.1 mm, ∼ 1.5 mm and ∼ 0.5 mm, respectively. The three modeling approximations studied have a negligible effect on the alpha particle 10 Gy isodose position, with displacements ⩽ 0.01 mm. Significance. This work quantitatively evaluates the relative importance of different parameters and approximations in Alpha-DaRT alpha dose calculations based on their impact not only on the dose variation at a given distance from the source but also on the displacement of clinically significant isodoses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".