Table 1.
Screening algorithms and commercially available assays
| Algorithm or assay (Screening population) | How it works |
| ROCA (asymptomatic general population) | 1 Compares a woman’s longitudinal CA-125 pattern to the change-point CA-125 profile seen in women with ovarian cancer and the flat CA-125 profiles seen in women without ovarian cancer[1] 2 Based on the ROCA result, women get triaged into one of three groups[1]: (1) Low Risk: continue annual CA-125 testing (2) Intermediate Risk: repeat CA-125 test 3 mo later (3) High Risk: receive TVS and referral to a gynecologic oncologist 3 After each additional CA-125 value, ROCA is recalculated and a new recommendation is made[1] |
| ROMA (known pelvic mass) | 1 Uses both HE-4 and CA-125 test levels to evaluate patients as low or high risk for ovarian cancer[8] 2 A predictive index (PI) is calculated using different equations for pre-menopausal and post-menopausal women[8] 3 The PI is then inserted into the ROMA algorithm to predict the probability of ovarian cancer[8] |
| RMI (known pelvic mass) | Uses menopausal status, ultrasound findings, and serum CA-125 levels to determine malignancy risk[40] |
| OVA1 (known pelvic mass) | 1 A multivariate index assay that incorporates CA-125, transferrin, transthyretin (prealbumin), apolipoprotein A1, and beta-2-microglobulin[41] 2 An algorithm is used to generate an ovarian malignancy risk score between 0 and 10[41] 3 OVA1 scores greater than or equal to 5.0 (premenopausal) or 4.4 (postmenopausal) result in high risk stratification and referral to a gynecologic oncologist[41] |
| LR-1 (known pelvic mass) | 1 An ultrasound-based prediction model 2 Twelve variables are used to calculate a probability of malignancy[88]: (1) personal history of ovarian cancer (2) current hormonal therapy (3) age of the patient (4) maximum diameter of the lesion (5) pain during examination (6) ascites (7) blood flow within a solid papillary projection (8) a purely solid tumor (9) maximum diameter of the solid component (10) irregular internal cyst walls (11) acoustic shadows (12) color score |
| LR-2 (known pelvic mass) | 1 An ultrasound-based prediction model 2 Uses six variables to calculate a probability of malignancy[90]: (1) patient’s age (2) presence of ascites (3) presence of blood flow within a papillary projection (4) maximal diameter of solid components (5) irregular internal cyst walls (6) presence of acoustic shadows |
ROCA: Risk of ovarian cancer algorithm; ROMA: Risk of ovarian malignancy algorithm; RMI: Risk of malignancy index; OVA1: Vermillion Inc. OVA1® blood test; LR-1: International ovarian tumor analysis logistic regression model 1;LR-2: International ovarian tumor analysis logistic regression model 2; ROCA: Risk of ovarian cancer algorithm; ROMA: Risk of Malignancy Algorithm.