Yang Zhenwei, Rizopoulos Dimitris, Heijnsdijk Eveline A M, Newcomb Lisa F, Erler Nicole S
Department of Biostatistics, Erasmus Medical Center Rotterdam, South Holland, the Netherlands.
Department of Epidemiology, Erasmus Medical Center Rotterdam, South Holland, the Netherlands.
Stat Med. 2025 May;44(10-12):e70134. doi: 10.1002/sim.70134.
Active surveillance (AS), where biopsies are conducted to detect cancer progression, has been acknowledged as an efficient way to reduce the overtreatment of prostate cancer. Most AS cohorts use fixed biopsy schedules for all patients. However, the ideal test frequency remains unknown, and the routine use of such invasive tests burdens the patients. An emerging idea is to generate personalized biopsy schedules based on each patient's progression-specific risk. To achieve that, we propose the interval-censored cause-specific joint model (ICJM), which models the impact of longitudinal biomarkers on cancer progression while considering the competing event of early treatment initiation. The underlying likelihood function incorporates the interval-censoring of cancer progression, the competing risk of treatment, and the uncertainty about whether cancer progression occurred since the last biopsy in patients that are right-censored or experience the competing event. The model can produce patient-specific risk profiles up to a horizon time. If the risk exceeds a certain threshold, a biopsy is conducted. The optimal threshold can be chosen by balancing two indicators of the biopsy schedules: The expected number of biopsies and the expected delay in detection of cancer progression. A simulation study showed that our personalized schedules could considerably reduce the number of biopsies per patient by 41%-52% compared to the fixed schedules, though at the cost of a slightly longer detection delay.
主动监测（AS），即通过活检来检测癌症进展，已被公认为是减少前列腺癌过度治疗的有效方法。大多数主动监测队列对所有患者都采用固定的活检时间表。然而，理想的检测频率仍然未知，并且这种侵入性检测的常规使用给患者带来了负担。一个新出现的想法是根据每个患者特定的进展风险制定个性化的活检时间表。为了实现这一点，我们提出了区间删失特定病因联合模型（ICJM），该模型在考虑早期开始治疗这一竞争事件的同时，对纵向生物标志物对癌症进展的影响进行建模。潜在的似然函数纳入了癌症进展的区间删失、治疗的竞争风险，以及在右删失或经历竞争事件的患者中自上次活检以来癌症是否进展的不确定性。该模型可以生成直至某个时间范围的患者特定风险概况。如果风险超过某个阈值，就进行活检。可以通过平衡活检时间表的两个指标来选择最佳阈值：活检的预期次数和癌症进展检测的预期延迟。一项模拟研究表明，与固定时间表相比，我们的个性化时间表可以将每位患者的活检次数大幅减少41% - 52%，不过代价是检测延迟略有延长。
