Ovarian cancer is a rare but very deadly disease: Its prevalence is about one eight that of breast cancer, but has much poorer overall 5-year survival rate; 38% for of ovarian cancer cases die within five years compared to 76% of breast cancer cases. However, when found with a comparable stage, women with ovarian cancer and breast cancer can expect comparable 5-year survival: 85% (ovarian) and 90% (breast) when local, and 18% (ovarian) and 19% (breast) when distant. The worse prognosis for ovarian cancer is due to the large proportion of ovarian cancers found in an advanced state; 25% of ovarian cancers are detected locally, compared to 49% of breast cancers. This gives a compelling case to improve ovarian cancer survival by detecting it earlier by screening.
Screening intends to reduce ovarian cancer mortality, but because it is a rare disease, any candidate screening program should also be cost effective to justify using it in the general population. Moreover, cancer screening is not a benign undertaking, and has its own risks and adverse effects. A positive screen can induce substantial stress on an individual, and so screening policies need to minimize the rates of false positives to control social burden. This is especially true for ovarian cancer, because a false positive test results in an invasive surgery on a woman who is without the disease. Currently, nearly twenty ovarian surgeries are performed for every cancer detected. Candidate strategies for ovarian cancer screening can make use of highly sensitive but unspecific imaging technology, either alone or combined with a specific but only marginally sensitive tumor marker (CA125). In the future, other tumor markers my also be used. We will use a comprehensive microsimulation model to study competing experimental strategies for ovarian cancer screening using these technologies. We will find that no single strategy is best on all performance measures; mortality reduction, cost-effectiveness, and social burden. We will also outline our research efforts for finding highly cost effective screening algorithms for ovarian cancer.
