Shen Shiwen, Han Simon X, Petousis Panayiotis, Weiss Robert E, Meng Frank, Bui Alex A T, Hsu William
Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
Comput Biol Med. 2017 Feb 1;81:111-120. doi: 10.1016/j.compbiomed.2016.12.011. Epub 2016 Dec 22.
A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.
越来越多被认为患癌风险较高的人现在正定期接受群体筛查。然而，诸如辐射暴露、过度诊断和过度治疗等明显危害凸显了需要更好的时间模型来预测谁应该接受筛查以及筛查的频率。平均停留时间（MST），即肿瘤可通过成像检测到但无明显临床症状的平均持续时间，是制定筛查政策的关键变量。长期以来，人们一直使用连续马尔可夫模型（CMM）和最大似然估计（MLE）来研究MST的估计。然而，许多传统方法假设成像数据没有观测误差，这是不太可能的，并且可能会使MST的估计产生偏差。此外，当数据稀疏时，MLE可能无法得到稳定的估计。为了解决这些缺点，我们提出了一种针对周期性癌症筛查数据的概率建模方法。我们首先使用三状态CMM模型对癌症状态转变进行建模，同时考虑观测误差。然后，我们在贝叶斯框架内联合估计MST和观测误差。我们还考虑纳入协变量以估计个体化的疾病进展率。我们的方法在参加国家肺癌筛查试验（NLST）的接受胸部X光筛查的参与者身上得到了验证，并使用后验预测p值和皮尔逊卡方检验进行了验证。与MLE相比，我们的模型对MST的估计更准确、更合理。
