Quantifying the potential benefit of CA 125 screening for ovarian cancer.

We develop a stochastic model of screening for ovarian cancer with serum levels of the CA 125 radioimmunoassay, a tumor-specific marker. The natural history of the disease is constructed using a four stage model characterized by a multivariate distribution for duration in each stage. Preserving its important features, we simplify the model to a function of two parameters; the average duration in stage I and the coefficient of variation of duration in each stage. A yearly screening program is superimposed using exponential CA 125 growth curves which result in stage-specific sensitivities corresponding to values reported in the literature. By implementing a computer simulation of the stochastic model, we estimate the benefit due to screening. This benefit is expressed as expected years of life saved per case of ovarian cancer. The model incorporates the stochastic nature of the disease process, allows easy analysis of repeated screens, and automatically accounts for correlation between subsequent tests. It provides the basis for planning optimal screening strategies with CA 125 testing.

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