Modeling therapeutic response to radioiodine in metastatic thyroid cancer: a proof-of-concept study for individualized medicine
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Dominique Barbolosi1, Ilyssa Summer1, Christophe Meille1, Raphaël Serre1, Antony Kelly2, Slimane Zerdoud3, Claire Bournaud4, Claire Schvartz5, Michel Toubeau6, Marie-Elisabeth Toubert7, Isabelle Keller8 and David Taïeb9
1SMARTc Pharmacokinetics Unit, Aix-Marseille Université, Inserm S911 CRO2, Marseille, France
2Service de Médecine Nucléaire, Centre Jean Perrin, 63011 Clermont-Ferrand Cedex 01, France
3Oncopole, 31059 Toulouse Cedex 9, France
4Hospices Civils de Lyon, Groupement Hospitalier Est, Service de Médecine Nucléaire, 69 677 Bron Cedex, France
5CLCC Institut Jean Godinot, 51056 REIMS CEDEX, France
6Service de Médecine Nucléaire, Centre Georges François Leclerc, 21079 Dijon Cedex, France
7Service de Médecine Nucléaire, Hôpital Saint-Louis APHP, 75475 Paris Cedex 10, France
8Service de Médecine Nucléaire, Hôpital Saint Antoine, APHP, 75012 Paris, France
9Service de Médecine Nucléaire, CHU La Timone, Aix-Marseille Université, 13385 Marseille 05, France
Dominique Barbolosi, email: firstname.lastname@example.org
Keywords: metastatic thyroid cancer, radioactive iodine therapy, personalized medicine, mathematical model, therapeutic nuclear medicine
Received: December 21, 2016 Accepted: February 18, 2017 Published: March 29, 2017
Purpose: Radioiodine therapy (RAI) has traditionally been used as treatment for metastatic thyroid cancer, based on its ability to concentrate iodine. Propositions to maximize tumor response with minimizing toxicity, must recognize the infinite possibilities of empirical tests. Therefore, an approach of this study was to build a mathematical model describing tumor growth with the kinetics of thyroglobulin (Tg) concentrations over time, following RAI for metastatic thyroid cancer.
Experimental Design: Data from 50 patients with metastatic papillary thyroid carcinoma treated within eight French institutions, followed over 3 years after initial RAI treatments, were included in the model. A semi-mechanistic mathematical model that describes the tumor growth under RAI treatment was designed.
Results: Our model was able to separate patients who responded to RAI from those who did not, concordant with the physicians’ determination of therapeutic response. The estimated tumor doubling-time (Td was found to be the most informative parameter for the distinction between responders and non-responders. The model was also able to reclassify particular patients in early treatment stages.
Conclusions: The results of the model present classification criteria that could indicate whether patients will respond or not to RAI treatment, and provide the opportunity to perform personalized management plans.
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