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使用隐马尔可夫模型对前列腺癌主动监测研究进行学习,比较活检样本不足和年度进展情况。

Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies.

机构信息

Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.

Department of Urology, University of Michigan, Ann Arbor, MI, USA.

出版信息

Cancer Med. 2020 Dec;9(24):9611-9619. doi: 10.1002/cam4.3549. Epub 2020 Nov 6.

Abstract

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohorts. For the four AS cohorts, the estimated annual progression rate was between 6%-13%. The estimated probability of a biopsy successfully sampling undiagnosed non-favorable risk cancer (biopsy sensitivity) was between 71% and 80%. In the simulation study of patients diagnosed with favorable risk cancer at age 50, the mean number of biopsies performed before age 75 was between 4.11 and 12.60, depending on the biopsy strategy. The mean delay time to detection of non-favorable risk cancer was between 0.38 and 2.17 years. Biopsy undersampling and progression varied considerably across study cohorts. There was no single best biopsy protocol that is optimal for all cohorts, because of the variation in biopsy under-sampling error and annual progression rates across cohorts. All strategies demonstrated diminishing benefits from additional biopsies.

摘要

本研究旨在估计莫菲特基金会全球行动计划前列腺癌主动监测(GAP3)联盟内四个前列腺癌(PCa)主动监测(AS)队列的活检样本不足率和进展率。我们使用隐马尔可夫模型(HMM)来估计定义 AS 男性 PCa 动力学的因素,包括纵向数据中隐含的活检样本不足和进展,这些数据来自 GAP3 数据库中包含的四个大型队列。HMM 随后被用作模拟模型的基础,以评估之前为每个队列提出的活检策略。对于四个 AS 队列,估计的年进展率在 6%-13%之间。估计成功采样未诊断的非有利风险癌症(活检敏感性)的活检概率在 71%-80%之间。在模拟研究中,50 岁时诊断为低危癌症的患者,75 岁前进行的活检次数平均在 4.11 到 12.60 之间,具体取决于活检策略。检测到非有利风险癌症的平均延迟时间在 0.38 到 2.17 年之间。活检样本不足和进展在研究队列之间存在显著差异。由于各队列活检样本不足误差和年进展率的差异,没有一种单一的最佳活检方案对所有队列都最优。所有策略的额外活检获益都在减少。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ba4/7774732/ac309d2bf25f/CAM4-9-9611-g001.jpg

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