Sensitivity of low energy brachytherapy Monte Carlo dose calculations to uncertainties in human tissue composition
Bibliographic record
Abstract
PURPOSE: The objective of this work is to assess the sensitivity of Monte Carlo (MC) dose calculations to uncertainties in human tissue composition for a range of low photon energy brachytherapy sources: 125I, 103Pd, 131Cs, and an electronic brachytherapy source (EBS). The low energy photons emitted by these sources make the dosimetry sensitive to variations in tissue atomic number due to the dominance of the photoelectric effect. This work reports dose to a small mass of water in medium D(w,m) as opposed to dose to a small mass of medium in medium D(m,m). METHODS: Mean adipose, mammary gland, and breast tissues (as uniform mixture of the aforementioned tissues) are investigated as well as compositions corresponding to one standard deviation from the mean. Prostate mean compositions from three different literature sources are also investigated. Three sets of MC simulations are performed with the GEANT4 code: (1) Dose calculations for idealized TG-43-like spherical geometries using point sources. Radial dose profiles obtained in different media are compared to assess the influence of compositional uncertainties. (2) Dose calculations for four clinical prostate LDR brachytherapy permanent seed implants using 125I seeds (Model 2301, Best Medical, Springfield, VA). The effect of varying the prostate composition in the planning target volume (PTV) is investigated by comparing PTV D90 values. (3) Dose calculations for four clinical breast LDR brachytherapy permanent seed implants using 103Pd seeds (Model 2335, Best Medical). The effects of varying the adipose/gland ratio in the PTV and of varying the elemental composition of adipose and gland within one standard deviation of the assumed mean composition are investigated by comparing PTV D90 values. For (2) and (3), the influence of using the mass density from CT scans instead of unit mass density is also assessed. RESULTS: Results from simulation (1) show that variations in the mean compositions of tissues affect low energy brachytherapy dosimetry. Dose differences between mean and one standard deviation of the mean composition increasing with distance from the source are observed. It is established that the 125I and 131Cs sources are the least sensitive to variations in elemental compositions while 103Pd is most sensitive. The EBS falls in between and exhibits complex behavior due to significant spectral hardening. Results from simulation (2) show that two prostate compositions are dosimetrically equivalent to water while the third shows D90 differences of up to 4%. Results from simulation (3) show that breast is more sensitive than prostate with dose variations of up to 30% from water for 70% adipose/30% gland breast. The variability of the breast composition adds a +/- 10% dose variation. CONCLUSIONS: Low energy brachytherapy dose distributions in tissue differ from water and are influenced by density, mean tissue composition, and patient-to-patient composition variations. The results support the use of a dose calculation algorithm accounting for heterogeneities such as MC. Since this work shows that variations in mean tissue compositions affect MC dosimetry and result in increased dose uncertainties, the authors conclude that imaging tools providing more accurate estimates of elemental compositions such as dual energy CT would be beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".