Sensitivity of low energy brachytherapy Monte Carlo dose calculations to uncertainties in human tissue composition
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».