Clinical performance of two multi-marker blood tests for predicting malignancy in women with an adnexal mass

Value of serum human epididymis secretory protein 4 as a marker for differential diagnosis of malignant and benign gynecological diseases of patients in southern China

“…However, HE4 display different results from those obtained by Moore et al whose positive rate was 12%, and by Ortiz-Munoz et al 12.6% in benign diseases [21]. Supplementary Table 1 summarized the recently studies of HE4, CA125, and ROMA for differentiating ovarian cancer from benign disease [14,[23][24][25][26][27][28][29][30][31]. However, these studies are not consistent with each other.…”

“…For ovarian cancer, ROMA demonstrated similar sensitivity and NPV to CA125, but higher specificity and PPV (specificity and PPV for ROMA compared to CA125, 80.6% vs. 70.2%, 54.1% vs. 43.6%, respectively). Many studies showed that ROMA algorithm displayed the best-balanced diagnostic performance to differentiate ovarian cancer from benign pelvic masses in the triage of patients to appropriate cancer centers for treatment by specialized gynecologic oncologists [14,[23][24][25][26][27][28][29][30][31] (Supplementary Table 1). However, these studies are not consistent with each other, may be caused by the different geographical origins, the different numbers of subjects investigated and the different measured systems used.…”

Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

“…The performed iTRAQ-based analyses identified several proteins, which have already been suggested as potential OC biomarkers (TF, SAA1, CRP, ALB) [20,21,22,23]. These findings confirmed that isobaric tags are a proper method for identifying differences in serum protein expression.…”

“…TF is already used in clinical practice as a part of a multi-marker serum test (OVA1) approved by the FDA for OC risk estimation. OVA1 test, apart from TF, is based on of CA125, transthyretin, apolipoprotein A-1, and β2-microglobulin [20]. Under-expression of TF in both, serum and cancer tissue samples, was also proved by a label-free LC-MS experiment [30].…”

Geographic disparities amongst patients with gynecologic malignancies at an urban NCI-designated cancer center

“…Average income information at the patient's home address zip code was extrapolated from census data [20]. Maps were created to show the geographic distribution of patients in relationship to the Cancer Center using Batchgeo software (http://batchgeo.com/).…”