Proust-Lima Cécile, Taylor Jeremy M G, Williams Scott G, Ankerst Donna P, Liu Ning, Kestin Larry L, Bae Kyounghwa, Sandler Howard M
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Int J Radiat Oncol Biol Phys. 2008 Nov 1;72(3):782-91. doi: 10.1016/j.ijrobp.2008.01.056.
To assess the relationship between prognostic factors, postradiation prostate-specific antigen (PSA) dynamics, and clinical failure after prostate cancer radiation therapy using contemporary statistical models.
Data from 4,247 patients with 40,324 PSA measurements treated with external beam radiation monotherapy in five cohorts were analyzed. Temporal change of PSA after treatment completion was described by a specially developed linear mixed model that included standard prognostic factors. These factors, along with predicted PSA evolution, were incorporated into a Cox model to establish their predictive value for the risk of clinical recurrence over time.
Consistent relationships were found across cohorts. The initial PSA decline after radiation therapy was associated with baseline PSA and T-stage (p < 0.001). The long-term PSA rise was associated with baseline PSA, T-stage, and Gleason score (p < 0.001). The risk of clinical recurrence increased with current level (p < 0.001) and current slope of PSA (p < 0.001). In a pooled analysis, higher doses of radiation were associated with a lower long-term PSA rise (p < 0.001) but not with the risk of recurrence after adjusting for PSA trajectory (p = 0.63). Conversely, after adjusting for other factors, increased age at diagnosis was not associated with long-term PSA rise (p = 0.85) but was directly associated with decreased risk of recurrence (p < 0.001).
We conclude that a linear mixed model can be reliably used to construct typical patient PSA profiles after prostate cancer radiation therapy. Pretreatment factors along with PSA evolution and the associated risk of recurrence provide an efficient and quantitative way to assess the impact of risk factors on disease progression.
使用当代统计模型评估前列腺癌放射治疗后预后因素、放疗后前列腺特异性抗原(PSA)动态变化与临床失败之间的关系。
分析了来自五个队列中4247例接受外照射单一疗法治疗的患者的40324次PSA测量数据。通过一个专门开发的线性混合模型描述治疗完成后PSA的时间变化,该模型纳入了标准预后因素。这些因素以及预测的PSA演变情况被纳入Cox模型,以确定它们对随时间临床复发风险的预测价值。
各队列中发现了一致的关系。放疗后最初的PSA下降与基线PSA和T分期相关(p<0.001)。长期的PSA上升与基线PSA、T分期和 Gleason评分相关(p<0.001)。临床复发风险随当前PSA水平(p<0.001)和当前PSA斜率(p<0.001)增加。在一项汇总分析中,更高剂量的放疗与更低的长期PSA上升相关(p<0.001),但在调整PSA轨迹后与复发风险无关(p=0.63)。相反,在调整其他因素后,诊断时年龄增加与长期PSA上升无关(p=0.85),但与复发风险降低直接相关(p<0.001)。
我们得出结论,线性混合模型可可靠地用于构建前列腺癌放射治疗后典型患者的PSA曲线。治疗前因素以及PSA演变情况和相关的复发风险提供了一种有效且定量的方法来评估风险因素对疾病进展的影响。