A Mathematical Model of Thyroid Disease Response to Radiotherapy

TL;DR

This paper introduces a biomathematical model of thyroid disease response to radiotherapy, combining deterministic and stochastic approaches to fit experimental data and explore personalized treatment strategies to improve tumor control.

Contribution

It presents a novel mechanistic model that integrates cellular damage dynamics and autoantibody responses, enabling personalized radiotherapy planning for thyroid diseases.

Findings

The model fits experimental data on thyroid volume and autoantibodies.

Individualized dose strategies increase tumor control probability.

Both deterministic and stochastic models provide complementary insights.

Abstract

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.