A computational framework for optimizing radioiodine therapy protocols in metastatic thyroid cancer

Thyroid cancer incidence is increasing, and radioactive iodine (RAI) therapy remains essential for the management of metastatic disease. However, marked inter-patient variability makes protocol selection challenging, particularly when balancing therapeutic efficacy against radioiodine induced toxicity. In this context, digital decision support tools can help explore alternative treatment strategies and support longitudinal monitoring. This study investigates the influence of key RAI protocol parameters (the number of treatment sessions (n), the interval between sessions ([Formula: see text]), and the administered activity per session (A)) on therapeutic response using a validated mechanistic compartmental model. A sensitivity analysis is performed to quantify how these parameters affect serum thyroglobulin ([Formula: see text]) kinetics, with particular emphasis on tumor cell doubling time ([Formula: see text]) under RAI exposure as a discriminating factor between responder and non-responder profiles. Based on this framework, a standalone freeware simulator (RAIR-Sim, RAItherapy Response Simulator) is developed to simulate protocol dependent [Formula: see text] trajectories from patient specific inputs and to support exploratory protocol planning. Future developments will focus on improving patient specific parameter estimation (including supervised learning strategies to update parameters at each session and account for their time variation) to further enhance individualized simulations.