A substantial rise in the number of cancer survivors has led to urgent management questions regarding effective posttreatment imaging-based surveillance strategies for cancer recurrence. Current surveillance guidelines provided by a number of professional societies all warn against overly aggressive surveillance, especially for low-risk patients, but all fail to provide more specific directions to accommodate underlying heterogeneity of cancer recurrence. Therefore it is imperative to develop data-driven strategies that can tailor the surveillance schedules to recurrence risk in this era of stricter insurance regulations, provider shortages, and rising costs of health care. Due to a lack of statistical methods for optimizing surveillance scheduling in presence of competing risks, we propose a general approach that uses an intuitive loss function for optimization of early detection of recurrence before death. The proposed strategies can tailor to patient risks of recurrence, in terms of both intensity and amount of surveillance. Using general three-state Markov models, our method is flexible and includes earlier works as special cases. We illustrate our method in both simulation studies and an application to breast cancer surveillance.

中文翻译：

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为癌症复发量身定制的最佳治疗后监测

癌症幸存者数量的大幅增加导致了关于有效的基于治疗后成像的癌症复发监测策略的紧急管理问题。许多专业协会提供的当前监测指南都警告不要过度积极的监测，尤其是对低风险患者，但都未能提供更具体的指导以适应癌症复发的潜在异质性。因此，在这个保险法规更严格、供应商短缺和医疗保健成本上升的时代，必须制定数据驱动的策略，以根据复发风险定制监测计划。由于缺乏在存在竞争风险的情况下优化监控调度的统计方法，我们提出了一种通用方法，该方法使用直观的损失函数来优化死亡前复发的早期检测。建议的策略可以根据监测的强度和数量来适应患者的复发风险。使用一般的三态马尔可夫模型，我们的方法很灵活，并且包括早期的工作作为特殊情况。我们在模拟研究和乳腺癌监测应用中说明了我们的方法。