Taghipour Sharareh, Caudrelier Laurent N, Miller Anthony B, Harvey Bart
Ryerson University, Department of Mechanical and Industrial Engineering, Toronto, ON, Canada (ST).
University of Toronto, Toronto, ON, Canada (LNC).
Med Decis Making. 2017 Feb;37(2):212-223. doi: 10.1177/0272989X16660711. Epub 2016 Jul 28.
Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken.
We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50-59 (older) and 40-49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation.
We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50-59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively.
The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.
对乳腺癌进展及各种风险的影响进行建模，有助于确定女性何时开始和结束筛查，以及筛查的频率。
我们使用隐马尔可夫过程对乳腺癌的自然进展进行建模，并纳入协变量的影响。患者为来自加拿大国家乳腺筛查研究的50 - 59岁（年龄较大）和40 - 49岁（年龄较小）的女性。我们纳入了现患癌症，估计了筛查敏感性和过度诊断率，并通过模拟对模型进行验证。
我们发现年龄较大的女性从健康状态转变为临床前状态以及其他死亡原因的发生率较高，但从临床前状态转变为临床状态的发生率较低。相反，年龄较小的女性从健康状态转变为临床前状态以及其他死亡原因的发生率较低，但从临床前状态转变为临床状态的发生率较高。不同的风险因素在不同年龄组中有显著差异。年龄较大和较小女性的平均停留时间分别为2.53年和2.96年。在研究组中，年龄较大和较小女性初次体格检查和乳腺X线摄影的敏感性分别为0.87和0.81，后续筛查的敏感性分别为0.78和0.53。在对照组中，年龄较大和较小女性初次体格检查的敏感性分别为0.769和0.671，50 - 59岁对照组后续体格检查的敏感性为0.37。年龄较大和较小女性的过度诊断上限分别为25%和27%。
目前的工作为在纳入现患癌症的情况下更好地对人群癌症发病率进行建模提供了基础。
