Calculation of electron dose to target cells in a complex environment by Monte Carlo code "CELLDOSE".

BACKGROUND

We used the Monte Carlo code "CELLDOSE" to assess the dose received by specific target cells from electron emissions in a complex environment. (131)I in a simulated thyroid was used as a model.

METHODS

Thyroid follicles were represented by 170 microm diameter spherical units made of a lumen of 150 microm diameter containing colloidal matter and a peripheral layer of 10 microm thick thyroid cells. Neighbouring follicles are 4 microm apart. (131)I was assumed to be homogeneously distributed in the lumen and absent in cells. We firstly assessed electron dose distribution in a single follicle. Then, we expanded the simulation by progressively adding neighbouring layers of follicles, so to reassess the electron dose to this single follicle implemented with the contribution of the added layers.

RESULTS

Electron dose gradient around a point source showed that the (131)I electron dose is close to zero after 2,100 microm. Therefore, we studied all contributions to the central follicle deriving from follicles within 12 orders of neighbourhood (15,624 follicles surrounding the central follicle). The dose to colloid of the single follicle was twice as high as the dose to thyroid cells. Even when all neighbours were taken into account, the dose in the central follicle remained heterogeneous. For a 1-Gy average dose to tissue, the dose to colloidal matter was 1.168 Gy, the dose to thyroid cells was 0.982 Gy, and the dose to the inter-follicular tissue was 0.895 Gy. Analysis of the different contributions to thyroid cell dose showed that 17.3% of the dose derived from the colloidal matter of their own follicle, while the remaining 82.7% was delivered by the surrounding follicles. On the basis of these data, it is shown that when different follicles contain different concentrations of (131)I, the impact in terms of cell dose heterogeneity can be important.

CONCLUSION

By means of (131)I in the thyroid as a theoretical model, we showed how a Monte Carlo code can be used to map electron dose deposit and build up the dose to target cells in a complex multi-source environment. This approach can be of considerable interest for comparing different radiopharmaceuticals as therapy agents in oncology.