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

Area of Science:

• Oncology
• Nuclear Medicine
• Biophysics

Background:

• Thyroid cancer incidence is rising globally.
• Radioactive iodine (RAI) therapy is crucial for metastatic thyroid cancer management.
• Inter-patient variability in RAI response complicates treatment protocol selection, necessitating a balance between efficacy and toxicity.

Purpose of the Study:

• To investigate the impact of key RAI protocol parameters on therapeutic response.
• To develop a digital tool for simulating RAI treatment outcomes.
• To support individualized treatment planning and monitoring in metastatic thyroid cancer.

Main Methods:

• Utilized a validated mechanistic compartmental model to simulate RAI therapy.
• Performed sensitivity analysis on parameters: number of sessions (n), interval ([Formula: see text]), and activity per session (A).
• Focused on serum thyroglobulin ([Formula: see text]) kinetics and tumor cell doubling time ([Formula: see text]) to differentiate responders and non-responders.

Main Results:

• Quantified the influence of RAI protocol parameters on [Formula: see text] kinetics.
• Identified tumor cell doubling time ([Formula: see text]) as a key factor in predicting response to RAI therapy.
• Developed RAIR-Sim, a freeware simulator for predicting protocol-dependent [Formula: see text] trajectories.

Conclusions:

• RAIR-Sim provides a framework for exploratory protocol planning in RAI therapy.
• The simulator aids in balancing therapeutic efficacy and radioiodine-induced toxicity.
• Future work includes enhancing patient-specific parameter estimation for improved individualized simulations.