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The thyroid cancer policy model: A mathematical simulation model of papillary thyroid carcinoma in The U.S. population

Author

Listed:
  • Carrie Lubitz
  • Ayman Ali
  • Tiannan Zhan
  • Curtis Heberle
  • Craig White
  • Yasuhiro Ito
  • Akira Miyauchi
  • G Scott Gazelle
  • Chung Yin Kong
  • Chin Hur

Abstract

Background: Thyroid cancer affects over ½ million people in the U.S. and the incidence of thyroid cancer has increased worldwide at a rate higher than any other cancer, while survival has remained largely unchanged. The aim of this research was to develop, calibrate and verify a mathematical disease model to simulate the natural history of papillary thyroid cancer, which will serve as a platform to assess the effectiveness of clinical and cancer control interventions. Methods: Herein, we modeled the natural pre-clinical course of both benign and malignant thyroid nodules with biologically relevant health states from normal to detected nodule. Using established calibration techniques, optimal parameter sets for tumor growth characteristics, development rate, and detection rate were used to fit Surveillance Epidemiology and End Results (SEER) incidence data and other calibration targets. Results: Model outputs compared to calibration targets demonstrating sufficient calibration fit and model validation are presented including primary targets of SEER incidence data and size distribution at detection of malignancy. Additionally, we show the predicted underlying benign and malignant prevalence of nodules in the population, the probability of detection based on size of nodule, and estimates of growth over time in both benign and malignant nodules. Conclusions: This comprehensive model provides a dynamic platform employable for future comparative effectiveness research. Future model analyses will test and assess various clinical management strategies to improve patient outcomes related to thyroid cancer and optimize resource utilization for patients with thyroid nodules.

Suggested Citation

  • Carrie Lubitz & Ayman Ali & Tiannan Zhan & Curtis Heberle & Craig White & Yasuhiro Ito & Akira Miyauchi & G Scott Gazelle & Chung Yin Kong & Chin Hur, 2017. "The thyroid cancer policy model: A mathematical simulation model of papillary thyroid carcinoma in The U.S. population," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0177068
    DOI: 10.1371/journal.pone.0177068
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    References listed on IDEAS

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    2. Sébastien Benzekry & Clare Lamont & Afshin Beheshti & Amanda Tracz & John M L Ebos & Lynn Hlatky & Philip Hahnfeldt, 2014. "Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-19, August.
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