Division of Urology, Department of Surgery, Yonghe Cardinal Hospital, New Taipei 23445, Taiwan.
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.
Genes (Basel). 2019 Aug 24;10(9):641. doi: 10.3390/genes10090641.
(1) Background: A simulation approach for prostate cancer (PrCa) with a prostate-specific antigen (PSA) test incorporating genetic information provides a new avenue for the development of personalized screening for PrCa. Going by the evidence-based principle, we use the simulation method to evaluate the effectiveness of mortality reduction resulting from PSA screening and its utilization using a personalized screening regime as opposed to a universal screening program. (2) Methods: A six-state (normal, over-detected, low-grade, and high-grade PrCa in pre-clinical phase, and low-grade and high-grade PrCa in clinical phase) Markov model with genetic and PSA information was developed after a systematic review of genetic variant studies and dose-dependent PSA studies. This gene‒PSA-guided model was used for personalized risk assessment and risk stratification. A computer-based simulated randomized controlled trial was designed to estimate the reduction of mortality achieved by three different screening methods, personalized screening, universal screening, and a non-screening group. (3) Results: The effectiveness of PrCa mortality reduction for a personalized screening program compared to a non-screening group (22% (9%‒33%)) was similar to that noted in the universal screening group (20% (7%‒21%). However, a personalized screening program could dispense with 26% of unnecessary PSA testing, and avoid over-detection by 2%. (4) Conclusions: Gene‒PSA-guided personalized screening for PrCa leads to fewer unnecessary PSA tests without compromising the benefits of mortality reduction (as happens with the universal screening program).
（1）背景：将包含遗传信息的前列腺特异性抗原（PSA）测试纳入前列腺癌（PrCa）模拟方法为前列腺癌的个体化筛查提供了新的途径。根据循证原则，我们使用模拟方法来评估 PSA 筛查及其利用通过个体化筛查方案而不是普遍筛查方案降低死亡率的效果。（2）方法：对遗传变异研究和剂量依赖性 PSA 研究进行系统回顾后，开发了一个具有遗传和 PSA 信息的六状态（正常、过度检测、低级别和高级别临床前期 PrCa、低级别和高级别临床期 PrCa）Markov 模型。该基因-PSA 指导模型用于个体化风险评估和风险分层。设计了基于计算机的模拟随机对照试验，以估计三种不同筛查方法（个体化筛查、普遍筛查和非筛查组）降低死亡率的效果。（3）结果：与非筛查组相比，个体化筛查方案降低 PrCa 死亡率的效果（22%（9%33%））与普遍筛查组相似（20%（7%21%）。然而，个体化筛查方案可以减少 26%的不必要的 PSA 检测，并减少 2%的过度检测。（4）结论：基于基因-PSA 的个体化筛查前列腺癌可减少不必要的 PSA 检测，而不会影响死亡率降低的益处（与普遍筛查方案一样）。
