Development of an ovarian cancer screening decision model that incorporates disease heterogeneity

Implications for potential mortality reduction

Authors

  • Laura J. Havrilesky MD, MHSc,

    Corresponding author
    1. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
    • Department of Obstetrics and Gynecology, Box 3079, Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC 27710
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    • Fax: (919)694-8719

  • Gillian D. Sanders PhD,

    1. Duke Evidence Based Practice Center, Duke University Medical Center, Durham, North Carolina
    2. Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina
    3. Department of Medicine, Duke University Medical Center, Durham, North Carolina
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  • Shalini Kulasingam PhD,

    1. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota
    2. School of Public Health, University of Minnesota, Minneapolis, Minnesota
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  • Junzo P. Chino MD,

    1. Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
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  • Andrew Berchuck MD,

    1. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
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  • Jeffrey R. Marks PhD,

    1. Department of Surgery, Duke University Medical Center, Durham, North Carolina
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  • Evan R. Myers MD, MPH

    1. Duke Evidence Based Practice Center, Duke University Medical Center, Durham, North Carolina
    2. Division of Clinical and Epidemiological Research, Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
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  • See editorial on pages 000–000, this issue.

Abstract

BACKGROUND:

Pathologic and genetic data suggest that epithelial ovarian cancer may consist of indolent and aggressive phenotypes. The objective of the current study was to estimate the impact of a 2-phenotype paradigm of epithelial ovarian cancer on the mortality reduction achievable using available screening technologies.

METHODS:

The authors modified a Markov model of ovarian cancer natural history (the 1-phenotype model) to incorporate aggressive and indolent phenotypes (the 2-phenotype model) based on histopathologic criteria. Stage distribution, incidence, and mortality were calibrated to data from the Surveillance, Epidemiology, and End Results Program of the US National Cancer Institute. For validation, a Monte Carlo microsimulation (1000,000 events) of the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) multimodality prevalence screen was performed. Mortality reduction and positive predictive value (PPV) were estimated for annual screening.

RESULTS:

In validation against UKCTOCS data, the model-predicted percentage of screen-detected cancers diagnosed at stage I and II was 41% compared with 47% (UKCTOCS data), and the model-predicted PPV of screening was 27% compared with 35% (UKCTOCS data). The model-estimated PPV of a strategy of annual population-based screening in the United States at ages 50 to 85 years was 14%. The mortality reduction using annual postmenopausal screening was 14.7% (1-phenotype model) and 10.9% (2-phenotype model). Mortality reduction was lower with the 2-phenotype model than with the 1-phenotype model regardless of screening frequency or test sensitivity; 68% of cancer deaths are accounted for by the aggressive phenotype.

CONCLUSIONS:

The current analysis suggested that reductions in ovarian cancer mortality using available screening technologies on an annual basis are likely to be modest. A model that incorporated 2 clinical phenotypes of ovarian carcinoma into its natural history predicted an even smaller potential reduction in mortality because of the more frequent diagnosis of indolent cancers at early stages. Cancer 2011. © 2010 American Cancer Society.

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