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根据累积实验室结果预测乳腺癌患者的复发:一种应用时间序列分析的新技术

Predicting recurrence in patients with breast cancer from cumulative laboratory results: a new technique for the application of time series analysis.

作者信息

Winkel P, Bentzon M W, Statland B E, Mouridsen H, Sheike O

出版信息

Clin Chem. 1982 Oct;28(10):2057-67.

PMID:7127734
Abstract

We followed the cases of 26 consecutive postmenopausal patients operated on for primary breast cancer. Serum specimens were obtained each month for 1.5 years and stored at -80 degrees C until assayed for carcinoembryonic antigen (CEA) and other quantities. Ten patients developed recurrence, while 16 qualified as controls (no clinical recurrence for at least 1.7 years after the last venipuncture). Using the homeostatic autoregressive time series model, modified by us to be particularly sensitive to sustained deviations from the mean, we detected four recurrences by CEA without having any falsely positive alarms. Group-based reference limits and application of the unmodified homeostatic model were less effective (fewer detected and shorter lead time). Simulation studies, involving use of a mathematical model relating CEA concentration to tumor growth and using parameters estimated from patient data, verified this and indicated that at least five stable baseline values are needed to detect 100% of recurrences before they are detected by the group-based limit.

摘要

我们对连续26例因原发性乳腺癌接受手术的绝经后患者进行了跟踪。在1.5年的时间里,每月采集血清样本,并在-80摄氏度下储存,直至检测癌胚抗原(CEA)及其他指标。10例患者出现复发,16例作为对照(最后一次静脉穿刺后至少1.7年无临床复发)。我们使用经修改后对均值持续偏差特别敏感的稳态自回归时间序列模型,通过CEA检测到4例复发,且无任何假阳性警报。基于组的参考限值和未修改的稳态模型的应用效果较差(检测到的复发较少且提前期较短)。模拟研究涉及使用一个将CEA浓度与肿瘤生长相关联的数学模型,并使用从患者数据估计的参数,证实了这一点,并表明在基于组的限值检测到复发之前,至少需要五个稳定的基线值才能100%检测到复发。

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