Purpose: The purpose of this work is to calculate individualized dose distributions in patients undergoing ¹⁸F‐FDG PET/CT studies through a methodology based on full Monte Carlo (MC) simulations and PET/CT patient images, and to compare such values with those obtained by employing nonindividualized phantom‐based methods.Methods: We developed a MC‐based methodology for individualized internal dose calculations, which relies on CT images (for organ segmentation and dose deposition), PET images (for organ segmentation and distributions of activities), and a biokinetic model (which works with information provided by PET and CT images) to obtain cumulated activities. The software vGATE version 8.1. was employed to carry out the Monte Carlo calculations. We also calculated deposited doses with nonindividualized phantom‐based methods (Cristy–Eckerman, Stabin, and ICRP‐133).Results: Median MC‐calculated dose/activity values are within 0.01–0.03 mGy/MBq for most organs, with higher doses delivered especially to the bladder wall, major vessels, and brain (medians of 0.058, 0.060, 0.066 mGy/MBq, respectively). Comparison with values obtained with nonindividualized phantom‐based methods has shown important differences in many cases (ranging from −80% to + 260%). These differences are significant (p < 0.05) for several organs/tissues, namely, remaining tissues, adrenals, bladder wall, bones, upper large intestine, heart, pancreas, skin, and stomach wall.Conclusions: The methodology presented in this work is a viable and useful method to calculate internal dose distributions in patients undergoing medical procedures involving radiopharmaceuticals, individually, with higher accuracy than phantom‐based methods, fulfilling the guidelines provided by the European Council directive 2013/59/Euratom.

Pharmacokinetic modeling of the radiopharmaceuticals used in molecular radiotherapy is an important step towards accurate radiation dosimetry of such therapies. In this paper, we present a pharmacokinetic model for CLR1404, a phospholipid ether analog that, labeled with ¹²⁴I/¹³¹I, has emerged as a promising theranostic agent. We follow a systematic approach for the model construction based on a decoupling process applied to previously published experimental data, and using the goodness-of-fit, Sobol’s sensitivity analysis, and the Akaike Information Criterion to construct the optimal form of the model, investigate potential simplifications, and study factor prioritization. This methodology was applied to previously published experimental human time-activity curves for 9 organs. The resulting model consists of 17 compartments involved in the CLR1404 metabolism. Activity dynamics in most tissues are well described by a blood contribution plus a two-compartment system, describing fast and slow uptakes. The model can fit both clinical and pre-clinical kinetic data of ¹²⁴I/¹³¹I. In addition, we have investigated how simple fits (exponential and biexponential) differ from the complete model. Such fits, despite providing a less accurate description of time-activity curves, may be a viable alternative when limited data is available in a practical case.

We present a mechanistic biomathematical model of molecular radiotherapy of thyroid disease. The general model consists of a set of differential equations describing the dynamics of different populations of thyroid cells with varying degrees of damage caused by radiotherapy (undamaged cells, sub-lethally damaged cells, doomed cells, and dead cells), as well as the dynamics of thyroglobulin and antithyroglobulin autoantibodies, which are important surrogates of treatment response. The model is presented in two flavours: on the one hand, as a deterministic continuous model, which is useful to fit populational data, and on the other hand, as a stochastic Markov model, which is particularly useful to investigate tumor control probabilities and treatment individualization. The model was used to fit the response dynamics (tumor/thyroid volumes, thyroglobulin and antithyroglobulin autoantibodies) observed in experimental studies of thyroid cancer and Graves’ disease treated with ${}^{131}$I-radiotherapy. A qualitative adequate fitting of the model to the experimental data was achieved. We also used the model to investigate treatment individualization strategies for differentiated thyroid cancer, aiming to improve the tumor control probability. We found that simple individualization strategies based on the absorbed dose in the tumor and tumor radiosensitivity (which are both magnitudes that can potentially be individually determined for every patient) can lead to an important raise of tumor control probabilities.