Ovarian Cancer Screening With the Risk Of Ovarian Cancer Algorithm (ROCA): Good, Bad, or Just Expensive?

Abstract

Abstract Objectives To measure the effectiveness of ovarian cancer screening using the Risk of Ovarian Cancer Algorithm (ROCA). Methods A Markov model was constructed based on the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). This model was used to predict the outcome of ovarian cancer screening with ROCA. Results The model predicted the ovarian cancer mortality from age 50 to age 85 to be 0.954% with a decrease in life expectancy of 0.178years (yrs) per person. Using data from the UKCTOCS the model predicted a similar reduction in mortality (11% vs. 10%), and similar curves for ovarian cancer mortality. Screening at age 50 for 20yrs reduced ovarian cancer mortality from 0.953% to 0.898%, an absolute decrease of 6%, yielding an increase in life expectancy of 0.0101yrs, preventing 55 deaths per 100,000 screened at a cost of $585,946 per life-yr. Screening for 30yrs reduced mortality from 0.954% to 0.872%, an absolute decrease of 9%, preventing 82 deaths at a cost of $763,970 per life-yr. Conclusion The ROCA test can improve the detection of early ovarian cancer but is not practical for screening in an average-risk population. We predict the ROCA test will reduce overall ovarian cancer mortality by 6% to 9% but at a substantial cost. For ROCA to be practical, the cost would need to be reduced ten-fold and would have only a marginal impact on mortality from ovarian cancer. This model supports the FDA's criticism of the ROCA test. Ovarian cancer screening may reduce mortality from ovarian cancer but is not cost effective.