RT Journal Article
T1 A mathematical model of thyroid disease response to radiotherapy
A1 Gago Arias, Araceli
A1 Neira, Sara
A1 Terragni, Filippo
A1 Pardo Montero, Juan
AB We present a mechanistic biomathematical model of molecular radiotherapy of thyroiddisease. The general model consists of a set of differential equations describing the dynamics ofdifferent 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 dynamicsof thyroglobulin and antithyroglobulin autoantibodies, which are important surrogates of treatmentresponse. The model is presented in two flavours: on the one hand, as a deterministic continuousmodel, 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 andantithyroglobulin autoantibodies) observed in experimental studies of thyroid cancer and Graves’disease treated with 131I-radiotherapy. A qualitative adequate fitting of the model to the experimentaldata was achieved. We also used the model to investigate treatment individualization strategies fordifferentiated thyroid cancer, aiming to improve the tumor control probability. We found that simpleindividualization 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) canlead to an important raise of tumor control probabilities.
PB MDPI
SN 2227-7390
YR 2021
FD 2021-10-01
LK https://hdl.handle.net/10016/34453
UL https://hdl.handle.net/10016/34453
LA eng
NO This project has received funding from the Instituto de Salud Carlos III (PI17/01428grant, FEDER co-funding). This project has received funding from the European Unions Horizon2020 research and innovation programme under the Marie Skodowska-Curie grant agreementNo 839135. This project has received funding from FEDER/Ministerio de Ciencia, Innovación yUniversidades;Agencia Estatal de Investigación, under grant MTM2017-84446-C2-2-R.
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RD 1 sept. 2024