The difference of scoring dose to water or tissues in Monte Carlo dose calculations for low energy brachytherapy photon sources
Bibliographic record
Abstract
PURPOSE: The goal of this work is to compare D(m,m) (radiation transported in medium; dose scored in medium) and D(w,m) (radiation transported in medium; dose scored in water) obtained from Monte Carlo (MC) simulations for a subset of human tissues of interest in low energy photon brachytherapy. Using low dose rate seeds and an electronic brachytherapy source (EBS), the authors quantify the large cavity theory conversion factors required. The authors also assess whether ap plying large cavity theory utilizing the sources' initial photon spectra and average photon energy induces errors related to spatial spectral variations. First, ideal spherical geometries were investigated, followed by clinical brachytherapy LDR seed implants for breast and prostate cancer patients. METHODS: Two types of dose calculations are performed with the GEANT4 MC code. (1) For several human tissues, dose profiles are obtained in spherical geometries centered on four types of low energy brachytherapy sources: 125I, 103Pd, and 131Cs seeds, as well as an EBS operating at 50 kV. Ratios of D(w,m) over D(m,m) are evaluated in the 0-6 cm range. In addition to mean tissue composition, compositions corresponding to one standard deviation from the mean are also studied. (2) Four clinical breast (using 103Pd) and prostate (using 125I) brachytherapy seed implants are considered. MC dose calculations are performed based on postimplant CT scans using prostate and breast tissue compositions. PTV D90 values are compared for D(w,m) and D(m,m). RESULTS: (1) Differences (D(w,m)/D(m,m)-1) of -3% to 70% are observed for the investigated tissues. For a given tissue, D(w,m)/D(m,m) is similar for all sources within 4% and does not vary more than 2% with distance due to very moderate spectral shifts. Variations of tissue composition about the assumed mean composition influence the conversion factors up to 38%. (2) The ratio of D90(w,m) over D90(m,m) for clinical implants matches D(w,m)/D(m,m) at 1 cm from the single point sources, CONCLUSIONS: Given the small variation with distance, using conversion factors based on the emitted photon spectrum (or its mean energy) of a given source introduces minimal error. The large differences observed between scoring schemes underline the need for guidelines on choice of media for dose reporting. Providing such guidelines is beyond the scope of this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".