Personalized Risk-AdaptIve Surveillance strategies in cancEr -- PRAISE

癌症生物标志物处于精准医学的前沿，提供了巨大的机会， 挑战特别是，用于检测癌症幸存者复发的生物标志物开发正在蓬勃发展， 通过连续生物标志物测量的监测测试提供了在某一点检测复发的机会， 当治疗可能是治愈性的。然而，频繁的生物标志物检测可能会造成更多的伤害比好处， 低风险的个人，由于不必要的测试的成本和并发症，并增加了错误的可能性， 导致不必要的治疗。不幸的是，针对个别患者的监测是一个 复杂的决策问题，需要了解生物标志物测量的异质性 在患者之间和患者体内的时间上。因此，使用一刀切的监督测试指南- 所有策略在大多数癌症中仍然是常见的，尽管它们的临床效用不确定。总体 拟议研究的目标是制定一个决策框架，以确定最佳的监督 癌症幸存者的治疗策略具体目标是：目标1。制定并评估一个连续的决策- 通过将预测建模的统计方法与经济学概念相结合， 信息（VOI）分析，以指导有关复发检测和治疗的个性化决策， 系列生物标志物检测，旨在优化患者的长期结局。这一目标将建立在 初步工作，并开发一个动态决策算法，使用积累的信息， 给予时间来更新预测和指导决策。该框架的广泛适用性将是 通过考虑三种不同的癌症监测环境来证明：结直肠癌（CRC），前列腺癌 癌症（PrCA）和慢性粒细胞白血病（CML），这些疾病在治疗过程中遇到了一系列决策问题。 癌症监测目标2.将此框架应用于现有的电子健康记录（EHR）和队列研究 数据，以确定用于检测针对高风险患者的CRC复发的风险适应性监测策略 并建议低风险患者的随访频率较低。目标3： 评估拟议的风险适应性监测策略与指南的比较有效性- 在CRC的基础上进行监测。目标4。使用现有数据评估框架的可推广性， 解决以下患者的最佳随访频率：（a）患有复发性PrCA的低风险男性， 可以安全地延迟一段延长的时间，和（B）CML的长期存活者，其实现长期的 但目前仍需经常监测。我们致力于解决癌症中的一个重要问题 生存护理使用的方法，以帮助解决临床医生和患者面临的不确定性， 面临着使用新的和不断发展的生物标志物技术来监测患者术后复发， 在原发性癌症中存活了下来

Cancer biomarkers are at the leading edge of Precision Medicine, and offer both tremendous opportunities and challenges. In particular, biomarker development to detect recurrence in cancer survivors is blossoming, as surveillance testing with serial biomarker measurements offers an opportunity to detect recurrence at a point when treatment may be curative. However, frequent biomarker testing may cause more harm than benefit for low-risk individuals, due to the costs and complications of unnecessary testing and increased likelihood of false positives leading to unnecessary treatment. Unfortunately, tailoring surveillance to individual patients is a complex decision-making problem that requires understanding the heterogeneity in biomarker measurements across patients and across time within patients. As a result, surveillance testing guidelines using one-size-fits- all strategies continue to be common in most cancers, despite their uncertain clinical utility. The overarching goal of the proposed research is to develop a decision-making framework to identify optimal surveillance strategies among cancer survivors. The specific aims are: Aim 1. Develop and evaluate a sequential decision- making framework by merging statistical methods for prediction modeling with economics concepts for value of information (VOI) analysis to guide individualized decisions about testing and treatment for recurrence using serial biomarker testing, with the goal of optimizing long-term patient outcomes. This aim will build on preliminary work and develop a dynamic decision-making algorithm that uses accumulated information at a given time to update predictions and guide decisions. The broad applicability of the framework will be demonstrated by considering three distinct cancer surveillance settings: colorectal cancer (CRC), prostate cancer (PrCA), and chronic myeloid leukemia (CML), which capture a range of decision-making problems in cancer surveillance. Aim 2. Apply this framework to existing electronic health record (EHR) and cohort study data to identify a risk-adaptive surveillance strategy for detecting CRC recurrence that targets high-risk patients for frequent follow-up and treatment, and recommends less frequent follow-up for low-risk patients. Aim 3. Assess the comparative effectiveness of the proposed risk-adaptive surveillance strategy versus guideline- based surveillance in CRC. Aim 4. Use existing data to evaluate the generalizability of the framework by addressing the optimal frequency of follow-up among (a) low-risk men with recurrent PrCA, for whom treatment may be safely delayed for a prolonged period, and (b) long-term survivors of CML, who achieve long-term remission but currently continue to be monitored frequently. We address a significant problem in cancer survivorship care using approaches to help resolve the uncertainty that clinicians and patients face when confronted with using new and evolving biomarker technologies to monitor for recurrence after patients have survived their primary cancer.