Cavity theory applications for kilovoltage cellular dosimetry
Bibliographic record
Abstract
Abstract Relationships between macroscopic (bulk tissue) and microscopic (cellular) dose descriptors are investigated using cavity theory and Monte Carlo (MC) simulations. Small, large, and multiple intermediate cavity theory (SCT, LCT, and ICT, respectively) approaches are considered for 20 to 370 keV incident photons; ICT is a sum of SCT and LCT contributions weighted by parameter d . Considering μ m-sized cavities of water in bulk tissue phantoms, different cavity theory approaches are evaluated via comparison of D w , m / D m , m (where D w , m is dose-to-water-in-medium and D m , m is dose-to-medium-in-medium) with MC results. The best overall agreement is achieved with an ICT approach in which d = ( 1 − e − β L ) / ( β L ) , where L is the mean chord length of the cavity and β is given by e − β R C S D A = 0.04 ( R CSDA is the continuous slowing down approximation range of an electron of energy equal to that of incident photons). Cell nucleus doses, D nuc , computed with this ICT approach are compared with those from MC simulations involving multicellular soft tissue models considering a representative range of cell/nucleus sizes and elemental compositions. In 91 % of cases, ICT and MC predictions agree within 3 % ; disagreement is at most 8.8%. These results suggest that cavity theory may be useful for linking doses from model-based dose calculation algorithms (MBDCAs) with energy deposition in cellular targets. Finally, based on the suggestion that clusters of water molecules associated with DNA are important radiobiological targets, two approaches for estimating dose-to-water by application of SCT to MC results for D m , m or D nuc are compared. Results for these two estimates differ by up to 35 % , demonstrating the sensitivity of energy deposition within a small volume of water in nucleus to the geometry and composition of its surroundings. In terms of the debate over the dose specification medium for MBDCAs, these results do not support conversion of D m , m to D w , m using SCT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".