Department of Urology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA.
Department of Urology, Stanford University, Stanford, CA, USA.
Eur Urol. 2018 Aug;74(2):211-217. doi: 10.1016/j.eururo.2018.01.017. Epub 2018 Feb 9.
For men on active surveillance for prostate cancer, utility of prostate-specific antigen (PSA) kinetics (PSAk) in predicting pathologic reclassification remains controversial.
To develop prediction methods for utilizing serial PSA and evaluate frequency of collection.
DESIGN, SETTING, AND PARTICIPANTS: Data were collected from men enrolled in the multicenter Canary Prostate Active Surveillance Study, for whom PSA data were measured and biopsies performed on prespecified schedules. We developed a PSAk parameter based on a linear mixed-effect model (LMEM) that accounted for serial PSA levels.
The association of diagnostic PSA and/or PSAk with time to reclassification (increase in cancer grade and/or volume) was evaluated using multivariable Cox proportional hazards models.
A total of 851 men met the study criteria; 255 (30%) had a reclassification event within 5 yr. Median follow-up was 3.7 yr. After adjusting for prostate size, time since diagnosis, biopsy parameters, and diagnostic PSA, PSAk was a significant predictor of reclassification (hazard ratio for each 0.10 increase in PSAk=1.6 [95% confidence interval 1.2-2.1, p<0.001]). The PSAk model improved stratification of risk prediction for the top and bottom deciles of risk over a model without PSAk. Model performance was essentially identical using PSA data measured every 6 mo to those measured every 3 mo. The major limitation is the reliability of reclassification as an end point, although it drives most treatment decisions.
PSAk calculated using an LMEM statistically significantly predicts biopsy reclassification. Models that use repeat PSA measurements outperform a model incorporating only diagnostic PSA. Model performance is similar using PSA assessed every 3 or 6 mo. If validated, these results should inform optimal incorporation of PSA trends into active surveillance protocols and risk calculators.
In this report, we looked at whether repeat prostate-specific antigen (PSA) measurements, or PSA kinetics, improve prediction of biopsy outcomes in men using active surveillance to manage localized prostate cancer. We found that in a large multicenter active surveillance cohort, PSA kinetics improves the prediction of surveillance biopsy outcome.
对于接受前列腺癌主动监测的男性，前列腺特异性抗原（PSA）动力学（PSAk）在预测病理重新分类方面的效用仍存在争议。
开发利用连续 PSA 的预测方法并评估采集频率。
设计、设置和参与者：数据来自参加多中心 Canary 前列腺主动监测研究的男性，他们的 PSA 数据按照预定的时间表进行测量和活检。我们基于线性混合效应模型（LMEM）开发了一个 PSAk 参数，该模型考虑了连续的 PSA 水平。
使用多变量 Cox 比例风险模型评估诊断 PSA 和/或 PSAk 与重新分类（癌症分级和/或体积增加）时间的关联。
共有 851 名男性符合研究标准；255 名（30%）在 5 年内发生了重新分类事件。中位随访时间为 3.7 年。在调整前列腺大小、诊断后时间、活检参数和诊断 PSA 后，PSAk 是重新分类的显著预测因子（每增加 0.10 PSAk 的风险比为 1.6[95%置信区间 1.2-2.1，p<0.001]）。与没有 PSAk 的模型相比，PSAk 模型显著改善了风险预测的分层，对于风险最高和最低的十分位数。使用每 6 个月测量一次 PSA 与每 3 个月测量一次 PSA 的模型性能基本相同。主要限制是重新分类作为终点的可靠性，尽管它驱动了大多数治疗决策。
使用 LMEM 计算的 PSAk 统计学上显著预测了活检重新分类。使用重复 PSA 测量的模型优于仅包含诊断 PSA 的模型。使用每 3 个月或每 6 个月评估的 PSA 进行评估时，模型性能相似。如果得到验证，这些结果应该为将 PSA 趋势最佳纳入主动监测方案和风险计算器提供信息。
在本报告中，我们研究了在使用主动监测管理局限性前列腺癌的男性中，重复前列腺特异性抗原（PSA）测量或 PSA 动力学是否可以改善活检结果的预测。我们发现，在一个大型多中心主动监测队列中，PSA 动力学改善了监测活检结果的预测。
