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<p>Thyroid cancer incidence is increasing, and radioactive iodine (<Emphasis FontCategory="NonProportional">RAI</Emphasis>) 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 <Emphasis FontCategory="NonProportional">RAI</Emphasis> protocol parameters (the number of treatment sessions (<i>n</i>), the interval between sessions (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\Delta t\)</EquationSource></InlineEquation>), and the administered activity per session (<i>A</i>)) on therapeutic response using a validated mechanistic compartmental model. A sensitivity analysis is performed to quantify how these parameters affect serum thyroglobulin (<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(T_g\)</EquationSource></InlineEquation>) kinetics, with particular emphasis on tumor cell doubling time (<InlineEquation ID="IEq3"><EquationSource Format="TEX">\(T_d\)</EquationSource></InlineEquation>) under <Emphasis FontCategory="NonProportional">RAI</Emphasis> exposure as a discriminating factor between responder and non-responder profiles. Based on this framework, a standalone freeware simulator (<Emphasis FontCategory="NonProportional">RAIR-Sim</Emphasis>, <Emphasis FontCategory="NonProportional">RAI</Emphasis>therapy <Emphasis FontCategory="NonProportional">R</Emphasis>esponse <Emphasis FontCategory="NonProportional">Sim</Emphasis>ulator) is developed to simulate protocol dependent <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(T_g\)</EquationSource></InlineEquation> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月14日

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  • Marie Fusella Giuntini,
  • Cyril Voyant,
  • David Taieb,
  • Dominique Barbolosi

摘要

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 (\(\Delta t\)), 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 (\(T_g\)) kinetics, with particular emphasis on tumor cell doubling time (\(T_d\)) 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 \(T_g\) 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.