Table II

Risk of ovarian cancer algorithm

• Detailed analysis of over 5,000 serum CA125 values involving 22,000 volunteers followed up for a median of 8.6 years in the study by Jacobs et al. (6, 20) revealed that CA125 levels in women without ovarian cancer were static or decreased with time while preclinical levels associated with malignancy tended to rise.
• This allowed the formulation of separate complex change-point statistical models of the behavior of serial preclinical CA125 levels for cases and controls. These models take into account a woman’s age-related risk of ovarian cancer and her CA125 profile with time (24, 25).
• The ROC for an individual is calculated using a computerized algorithm based on the Bayes theorem, which compares each individuals serial CA125 levels to the pattern in cases compared to controls.
• The closer the CA125 profile to the CA125 behavior of known cases of ovarian cancer, the greater the risk of ovarian cancer. The final result is presented as the individual’s estimated risk of having ovarian cancer so that a ROC of 2% implies a risk of 1 in 50.