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The Effectiveness of Ovarian Cancer Screening: A Decision Analysis Model

Marilyn M. Schapira, MD, MPH; David B. Matchar, MD; and Mark J. Young, MD
[+] Article and Author Information

From the Medical College of Wisconsin, Milwaukee, Wisconsin; Duke University Medical Center, Durham, North Carolina; Henry Ford Hospital, Detroit, Michigan. Requests for Reprints: Marilyn M. Schapira, MD, Section of General Internal Medicine, 5000 W. National Avenue, Clement J. Zablocki Veterans Affairs Medical Center, 111-B, Milwaukee, WI 53295-1000. Acknowledgments: The authors thank John Pauk, MD, MPH, for review of the manuscript.


Copyright 2004 by the American College of Physicians


Ann Intern Med. 1993;118(11):838-843. doi:10.7326/0003-4819-118-11-199306010-00002
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Objective: To estimate the effectiveness of ovarian cancer screening with CA 125 and transvaginal sonography.

Design: Decision analysis was used to examine the no-screen compared with the screen strategy.

Setting: Estimates of cancer incidence, survival, and life expectancy were derived from population-based data and clinical series.

Subjects: A cohort of 40-year-old women of all races and residing in the United States.

Interventions: A one-time screening intervention. The criterion standard for diagnosis of ovarian cancer was evaluation with exploratory laparotomy.

Main Outcome Measure: Average years of life expectancy gained by women in the screened group.

Results: Screening for ovarian cancer with a combination of CA 125 and transvaginal sonography increases the average life expectancy in the population by less than 1 day.

Conclusions: Given the limited effect on overall life expectancy, it is unlikely that mass screening for ovarian cancer with CA 125 and transvaginal sonography would be an effective health policy.

Figures

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Figure 1.
Ovarian cancer decision tree.

The decision tree is designed to test the strategy of no-screen compared with a one-time screen for a population of healthy 40-year-old women. There are two options at the decision node: no-screen or screen. A series of chance nodes represent the following points of uncertainty: the likelihood of disease, the percentage of prevalent disease in the early stage at the time of the screen, the clinical detection rate of early disease, the detection rate of early disease with the screening strategy, the specificity of the screening test, and the mortality rate associated with diagnostic laparotomy. Assigned to each terminal node is the life expectancy for an individual whose experience corresponds to that path in the decision tree. These include the life expectancy of a 40-year-old woman with no disease, with early-stage disease, and with late-stage disease. For health states that require a laparotomy, 1 week is subtracted from life expectancy. For health states in which disease had progressed from early- to late-stage before diagnosis, 1 year is added to life expectancy.

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