In 2014, can we do better than CA125 in the early detection of ovarian cancer?

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.

Table 2 Specificity and sensitivity results of various screening strategies in the setting of a pelvic mass

Algorithm or assay	Ref.	Sensitivity (%)	Specificity (%)
ROMA	Karlsen et al[44]	94.4	76.5
	Moore et al[45]	94.3	75
	Sandri et al[46]	91.2	75
		89.3	81.7
	Van Gorp et al[89]	84.7	76.8
	Sandri et al[46]	84.4	90
	Chan et al[47]	89.2	87.3
	Kaijser et al[90]	84	80
RMI	Karlsen et al[44]	94.4	81.5
	Håkansson et al[48]	92	82
	Moore et al[45]	84.6	75
	Van den Akker[49]	81	85
	Van Gorp et al[89]	76	92.4
OVA1	Bristow et al[50]	92.4	53.5
	Longoria et al[52]	92.2	49.4
OVA1 +	Bristow et al[50]	95.7	50.7
Clinical assessment	Longoria et al[52]	95.3	44.2
LR-1	Kaijser et al[88]	93	77
LR-2	Nunes et al[51]	97	69
	Kaijser et al[88]	92	75
	Kaijser et al[90]	93.8	81.9
TVS	van Nagell et al[8]	86.4	98.8

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; TVS: Transvaginal ultrasonography.

Table 3 Results of serum marker panels for the detection of ovarian cancer

Serum marker(s)	Ref.	Sensitivity (%)	Specificity (%)
CA-125	1Karlsen et al[44]	91.7	75
	1Chan et al[47]	90.8	67.2
	1Leung et al[123]	89	90
	1Sandri et al[46]	84.4	80
	1Montagnana et al[54]	83	100
	1Sandri et al[46]	73.1	90
	Yang et al[55]	62.5	80
	Havrilesky et al[56]	45.9-58.5	98.2
	1Moore et al[37]	43.3	95
	Jacob et al[57]	12.5	90.1-93.9
HE-4	1Montagnana et al[54]	98	100
	Yang et al[55]	96.2	83.8
	1Karlsen et al[44]	91.3	75
	1Sandri et al[46]	83.1	90
	Havrilesky et al[56]	82.7-92.5	86.3
	1Moore et al[37]	72.9	95
	Jacob et al[57]	62.5	81.8-85.9
	1Chan et al[47]	56.9	96.9
CA-125, HE-4	1Moore et al[37]	76.4	95
	1Moore et al[41]	88.7	74.7
CA 125, leptin, PRL, OPN, IGFII, MIF	Visintin et al[58]	95.3	99.4
CA 125, CRP, SAA, IL-6, IL-8	Edgell et al[59]	94.1	91.3
CA-125, apoA-I, TTR, TF	Su et al[60]	89-97	91-99
CA 125, HE4, CEA, VCAM-1	Yurkovetsky et al[61]	86–93	98
CA 125, ApoA1, TTR	Kim et al[62]	93.9	95
	Zhang et al[42]	74	97
CA 125, CA 19-9, EGFR, CRP, myoglobin, ApoA1, ApoCIII, MIP-1a, IL-6, IL-18, tenascin C	1Amonkar et al[63]	91.3	88.5
CA-125, OVX1r, LASA,CA15-3, CA72-4	Nossov et al[11]	90.6	93.2
CA 125, CA 72-4, CA 15-3, M-CSF	1Skates et al[64]	70	98
LPA	Nossov et al[11]	90-100	90
FOLR1	1Leung et al[123]	62	90
M-CSF	Nossov et al[11]	61-68	93
SMRP	1Moore et al[37]	53.7	95

¹Study involved patients presenting with a pelvic mass; CA: Cancer antigen; HE-4: Human epididymis protein 4; PRL: Prolactin; OPN: Osteopontin; IGFII: Insulin-like growth factor II; MIF: Macrophage inhibitory factor; CRP: C-reactive protein; SAA: Serum amyloid A; IL: Interleukin; apoA-I: Apolipoprotein A-I; TTR: Transthyretin; TF: Transferrin; CEA: Carcinoembryonic antigen; VCAM-1: Vascular cell adhesion protein 1; EGFR: Epidermal growth factor receptor; ApoCIII: Apolipoprotein CIII; MIP-1a: Macrophage inflammatory protein-1alph; OVX1: Mouse antibody generated by immunizing mice with antigenic preparations from multiple OC cell lines[21]; LASA: Lipid-associated sialic acid; M-CSF: Macrophage colony-stimulating factor; LPA: Lipoprotein A; FOLR1: Folate receptor 1; SMRP: Soluble mesothelin-related peptide.