  We present a mechanistic biomathematical model of molecular radiotherapy of
thyroid disease. The general model consists of a set of differential equations
describing the dynamics of different populations of thyroid cells with varying
degrees of damage caused by radiotherapy (undamaged cells, sub-lethally damaged
cells, doomed cells, and dead cells), as well as the dynamics of thyroglobulin
and antithyroglobulin autoantibodies, which are important surrogates of
treatment response. The model is presented in two flavours: on the one hand, as
a deterministic continuous model, which is useful to fit populational data, and
on the other hand, as a stochastic Markov model, which is particularly useful
to investigate tumor control probabilities and treatment individualization. The
model was used to fit the response dynamics (tumor/thyroid volumes,
thyroglobulin and antithyroglobulin autoantibodies) observed in experimental
studies of thyroid cancer and Graves' disease treated with I-131-radiotherapy.
A qualitative adequate fitting of the model to the experimental data was
achieved. We also used the model to investigate treatment individualization
strategies for differentiated thyroid cancer, aiming to improve the tumor
control probability. We found that simple individualization strategies based on
the absorbed dose in the tumor and tumor radiosensitivity (which are both
magnitudes that can potentially be individually determined for every patient)
can lead to an important raise of tumor control probabilities.
