Lubitz Carrie, Ali Ayman, Zhan Tiannan, Heberle Curtis, White Craig, Ito Yasuhiro, Miyauchi Akira, Gazelle G Scott, Kong Chung Yin, Hur Chin
Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
PLoS One. 2017 May 8;12(5):e0177068. doi: 10.1371/journal.pone.0177068. eCollection 2017.
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.
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.
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.
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.
甲状腺癌在美国影响着超过50万人，并且在全球范围内，甲状腺癌的发病率增长速度高于其他任何癌症，而生存率基本保持不变。本研究的目的是开发、校准和验证一个数学疾病模型，以模拟甲状腺乳头状癌的自然病程，该模型将作为评估临床和癌症控制干预措施有效性的平台。
在此，我们对良性和恶性甲状腺结节的自然临床前病程进行建模，采用从正常到检测到结节的具有生物学相关性的健康状态。使用既定的校准技术，针对肿瘤生长特征、发展速率和检测率的最佳参数集用于拟合监测、流行病学和最终结果（SEER）发病率数据及其他校准目标。
展示了与校准目标相比的模型输出，证明了充分的校准拟合和模型验证，包括SEER发病率数据的主要目标以及恶性肿瘤检测时的大小分布。此外，我们还展示了人群中结节的预测潜在良性和恶性患病率、基于结节大小的检测概率以及良性和恶性结节随时间的生长估计。
这个综合模型为未来的比较效果研究提供了一个可使用的动态平台。未来的模型分析将测试和评估各种临床管理策略，以改善与甲状腺癌相关的患者预后，并优化甲状腺结节患者的资源利用。
