Incorporating microdosimetry into radiation therapy treatment planning with multi-scale Monte Carlo simulations
Bibliographic record
Abstract
In order to choose and design optimal treatment plans for radiation therapy, it is necessary to employ models that can predict the tissue response to the ionizing radiation. The conventional models are based on the radiological absorbed dose, which does not account for the effect of radiation damage clustering on cellular response – a more mechanistic model can lead to better metrics for treatment planning. This dissertation presents a novel method for performing multi-scale Monte Carlo simulations to obtain microdosimetric information for patient specific treatments. This is done by using a track structure Monte Carlo simulation in the regions of interest for scoring and a condensed history algorithm for the rest of the geometry. Since the condensed history code does not correctly follow the tracks of particles below a certain energy threshold, the volume in which the track structure simulation is performed must extend beyond the volume in which scoring is done. The effect of this extended volume on simulation accuracy and performance are discussed, and it is shown that the watch volume must extend beyond the target by a distance equal to the range of the subthreshold electrons. This simulation method is benchmarked against experimental measurements for several radioisotopes and run for a volumetric arc radiotherapy plan. In addition, there is a comparison of the microdosimetric characteristics of two widely used track structure simulations(Geant4-DNA and NOREC), and a discussion of the use of Monte Carlo in the patient specific treatment planning for Stereotactic Body Radiotherapy and Total Body Irradiation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".