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The Bayesian identification of a linear regression model (called the biphasic model) for time dependence of thyroid gland activity in 131I radioiodine therapy is presented. Prior knowledge is elicited via hard parameter constraints and via the merging of external information from an archive of patient records. This prior regularization is shown to be crucial in the reported context, where data typically comprise only two or three high-noise measurements. The posterior distribution is simulated via a Langevin diffusion algorithm, whose optimization for the thyroid activity application is explained. Excellent patient-specific predictions of thyroid activity are reported. The posterior inference of the patient-specific total radiation dose is computed, allowing the uncertainty of the dose to be quantified in a consistent form. The relevance of this work in clinical practice is explained. Keywords: Biphasic model, Prior constraints, External information, Langevin diffusion, Nonparametric stopping rule, Probabilistic dose estimatio

Identification of thyroid gland activity in radioiodine therapy