TY - JOUR
T1 - The thyroid cancer policy model
T2 - A mathematical simulation model of papillary thyroid carcinoma in The U.S. population
AU - Lubitz, Carrie
AU - Ali, Ayman
AU - Zhan, Tiannan
AU - Heberle, Curtis
AU - White, Craig
AU - Ito, Yasuhiro
AU - Miyauchi, Akira
AU - Gazelle, G. Scott
AU - Kong, Chung Yin
AU - Hur, Chin
N1 - Publisher Copyright:
Copyright © 2017 Lubitz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/5
Y1 - 2017/5
N2 - Background: Thyroid cancer affects over 1/2 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.
AB - Background: Thyroid cancer affects over 1/2 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.
UR - https://www.scopus.com/pages/publications/85019030578
U2 - 10.1371/journal.pone.0177068
DO - 10.1371/journal.pone.0177068
M3 - Article
C2 - 28481909
AN - SCOPUS:85019030578
SN - 1932-6203
VL - 12
JO - PLoS ONE
JF - PLoS ONE
IS - 5
M1 - e0177068
ER -