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Comparing adaptive and non-adaptive algorithms for cancer early detection with novel biomarkers

机译:比较使用新型生物标记物进行癌症早期检测的自适应和非自适应算法

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It may be possible to reduce cancer mortality by monitoring the concentrations of serum biomarkers over time in men and women to detect their cancer early, when it is most curable. The simplest approach to using a biomarker for screening is to sequentially use fixed thresholds as a means to determine an abnormal test (e.g., PSA exceeding 4 mg/ml, CA 125 exceeding 30 U/ml). Alternatives to the simplest single threshold (ST) rules include more sophisticated algorithms that make use of screening history that accumulates over time and determines abnormal tests using individualized reference ranges. Although in principle longitudinal algorithms should out perform fixed threshold rules, the actual benefit gained will depend on behavior of the biomarker, the screening algorithm, and the screening frequency. Little information has been available to help predict when conditions should compel the adoption of the more sophisticated algorithms and when conditions suggest the simpler algorithms should suffice, or indeed be preferred. In this manuscript we evaluate the conditions under which one should expect great benefit, and when one should not expect benefit, by comparing the ability of simple and complex algorithms to detect cancer early under a variety of biomarker behaviors and screening frequencies.
机译:通过监测男性和女性随着时间的推移血清生物标志物的浓度以尽早发现可治愈的癌症,可以降低癌症死亡率。使用生物标志物进行筛选的最简单方法是顺序使用固定阈值作为确定异常检测的手段(例如PSA超过4 mg / ml,CA 125超过30 U / ml)。最简单的单一阈值(ST)规则的替代方法包括更复杂的算法,这些算法利用了随时间累积的筛选历史记录,并使用个性化参考范围确定异常测试。尽管原则上纵向算法应该执行固定的阈值规则,但是实际获得的收益将取决于生物标记的行为,筛选算法和筛选频率。几乎没有信息可用来帮助预测何时条件将迫使采用更复杂的算法,以及何时条件表明较简单的算法应已满足,甚至是首选。在此手稿中,我们通过比较简单算法和复杂算法在各种生物标记物行为和筛查频率下早期检测癌症的能力,来评估一个人应该期望获得巨大收益的条件,以及一个人们不应该期望获得收益的条件。

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