Ovarian cancer is the fifth most common cause of cancer-related mortality in women. The main reason is the delayed diagnosis, which limits therapeutic options. There is still no sufficiently reliable screening algorithm for early stratification of high-risk patients and timely referral to specialized oncology centers. There has been ongoing extensive two-decade long research on various protein signatures, multimarker panels, scoring systems, and algorithms combining clinical, biochemical, and imaging data to identify an optimal approach for a sensitive and specific method for early detection of ovarian cancer. This review presents up-to-date information on available multimarker models and discusses their diagnostic sensitivity and specificity for reliable clinical application. It includes data from meta-analyses evaluating the clinical significance of the ROMA algorithm, CPH-I, MMP, OVA1, and OVERA, in comparison with standalone testing of established tumor biomarkers CA125 and HE4.
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