Title : Developing a digital decision-support tool for personalized treatment of metastatic thyroid cancer
Abstract:
Each year, a high number of new cases of thyroid cancer are identified. Although this disease has a good prognosis, it is often immediately metastatic, which requires removal of the thyroid body, followed by iratherapy (administration of iodine 131) in order to eradicate the various metastatic sites present.
Until now, iodine 131 administration protocols (activities of iodine 131 to be administered, number of iratherapy sessions and interval between two consecutive sessions) are conducted empirically and a large inter-individual variability in responses to treatment has been observed as well as toxicities induced in the more or less long term. In order to optimize the effectiveness-toxicity balance, it is essential to provide clinicians with a decision support tool allowing them to perform in silico simulations of the evolution of thyroglobulin concentrations depending on the chosen diode 131 administration protocol by the therapist.
An in silico simulator will be presented built from a mathematical model managed by a set of parameters whose values are specific to each individual, in particular the parameter (Td) of the doubling time of tumor cells under treatment is identified as a parameter key allowing discrimination from the first weeks of treatment, responding patients and patients refractory to iodine 131. In addition, in the case of a given responding patient, the simulator then makes it possible to propose to the clinician a diagram effective administration while using the minimum amount of iodine 131, so as to minimize the probability of the appearance of possible iatrogenic pathologies induced by excessive irradiation.
The precision of this personalized therapeutic planning simulator is conditioned by a good estimation of the individual parameters of each patient, in particular the Td parameter. In this sense, the use of Artificial Intelligence could help to refine the estimation of these parameters.
Additionally, this work provides proof of concept that the development of powerful digital tools could help enhance the precision of personalized medicine.
