A 2-stage ovarian cancer screening strategy using the risk of ovarian cancer algorithm (ROCA) identifies early-stage incident cancers and demonstrates high positive predictive value

摘要

Diagnosis of ovarian cancer at an early stage is associated with a rate of survival of 75% or higher. Most women with this cancer are diagnosed at a late stage when long-term cure rates are less than 30%. Currently, there are no proven screening strategies for the early detection for ovarian cancer. A definitive diagnosis requires invasive surgery and removal of the ovaries. Any screening strategy for this cancer must minimize false positives to decrease the number of unnecessary operations. Measurement of fixed cut-point levels of carbohydrate antigen 125 (CA-125), an ovarian tumor marker, and an abnormal transvaginal ultrasound (TVS) finding have been used as a screening strategy for ovarian cancer; however, neither has sufficient specificity. Recent studies have shown that the sensitivity and specificity of screening with CA-125 can be improved when rising CA-125 levels, even within a reference range, are used to prompt TVS. The Risk of Ovarian Cancer Algorithm (ROCA) is a computer algorithm that quantifies the risk of developing ovarian cancer in postmenopausal women at normal risk who are screened for this cancer.