Energy deposition clustering as a functional radiation quality descriptor for modeling relative biological effectiveness
Bibliographic record
Abstract
Purpose: To explore the use of the frequency of the energy deposition (ED) clusters of different sizes (cluster order, CO) as a surrogate (instead of, e.g., LET) classification of the physical characteristics of ionizing radiation at a nanometer scale, to construct a framework for the calculation of relative biological effectiveness (RBE) with cell survival as endpoint. Methods: The frequency of cluster order fCO is calculated by sorting the ED sites generated with the Monte Carlo track structure code LIonTrack into clusters based on a single parameter called the cluster distance dC being the maximum allowed distance between two neighboring EDs belonging to a cluster. Published cell survival data parameterized with the linear‐quadratic (LQ) model for V79 cells exposed to 15 different radiation qualities (including brachytherapy sources, proton, and carbon ions) were used as input to a fitting procedure, designed to determine a weighting function wCO that describes the capacity of a cluster of a certain CO to damage the cell's sensitive volume. The proposed framework uses both fCO and wCO to construct surrogate based functions for the LQ parameters α and β from which RBE values can be derived. Results: The results demonstrate that radiation quality independent weights wCO exist for both the α and β parameters. This enables the calculation of α values that correlate to their experimental counterparts within experimental uncertainties (relative residual of 15% for dC = 2.5 nm). The combination of both α and β surrogate based functions, despite the higher relative residuals for β values, yielded an RBE function that correlated to experimentally derived RBE values (relative residual of 16.5% for dC = 2.5 nm) for all radiation qualities included in this work. Conclusions: The fCO cluster characterization of ionizing radiation at a nanometer scale can effectively be used to calculate particle and energy dependent α and β values to predict RBE values with potential applications to, e.g., treatment planning systems in radiotherapy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".