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    Optimal Breast Cancer Screening Strategy for 40-Somethings Still Unclear

    WebMD Health News

    Dec. 2, 1999 (Chicago) -- In recent years, several groups have adopted guidelines suggesting annual breast cancer screening using mammograms for women aged 40-49. But while many practitioners have adopted this practice, others argue that it is not cost-effective and causes undue anxiety in many women.

    Now a sophisticated computer model developed by Sylvia K. Plevritis, PhD, an assistant professor at Stanford University Medical School, suggests that there is no easy answer when it comes to screening this group of women. Plevritis tells WebMD that if all mammography were created equal, "biennial screening would be the most cost-effective approach." But given the current wide variation among mammographers, a mammogram once every two years may not be enough, she reported at the 85th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA).

    Plevritis says, "If mammography was really as good as mammographers like to say it is, and if it could really detect lesions that are 1 cm or less, then recommending a once-every-two-years screening would probably be effective." Although Plevritis says she is unwilling to make any suggestion for a screening approach, she says that the current approach of yearly mammograms for women in the fifth decade is probably the most practical option.

    The problem, nonetheless, is that "there are many false positives and with those false positives comes anxiety, sometimes very great anxiety." These false positives occur when a radiologist believes the film shows cancer only to later discover, with further testing, they were wrong in their diagnosis. Plevritis says critics of a yearly screening approach claim the increased anxiety outweighs any potential advantage of yearly screening. For herself, she is unwilling to commit other than to say, "there are just too many variables. The point is that we still don't really know about doubling time and other factors."

    In her complex computer model, Plevritis attempted to take the guesswork out of the issue. The model simulates the natural history of breast cancer. Additionally, she factored in survival curves based on age and disease stage at diagnosis. Those natural history data were embedded into a computer model that simulates health and economic outcomes of various breast cancer screening strategies.

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