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. 2015 Nov;139(2):253-60.
doi: 10.1016/j.ygyno.2015.08.024. Epub 2015 Sep 3.

Identifying post-menopausal women at elevated risk for epithelial ovarian cancer

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Identifying post-menopausal women at elevated risk for epithelial ovarian cancer

Nicole Urban et al. Gynecol Oncol. 2015 Nov.

Abstract

Objective: We developed and validated a hybrid risk classifier combining serum markers and epidemiologic risk factors to identify post-menopausal women at elevated risk for invasive fallopian tube, primary peritoneal, and ovarian epithelial carcinoma.

Methods: To select epidemiologic risk factors for use in the classifier, Cox proportional hazards analyses were conducted using 74,786 Women's Health Initiative (WHI) Observational Study (OS) participants. To construct a combination classifier, 210 WHI OS cases and 536 matched controls with serum marker measurements were analyzed; validation employed 143 cases and 725 matched controls from the WHI Clinical Trial (CT) with similar data.

Results: Analyses identified a combination risk classifier composed of two elevated-risk groups: 1) women with CA125 or HE4 exceeding a 98% specificity threshold; and 2) women with intact fallopian tubes, prior use of menopausal hormone therapy for at least two years, and either a first degree relative with breast or ovarian cancer or a personal history of breast cancer. In the WHI OS population, it classified 13% of women as elevated risk, identifying 30% of ovarian cancers diagnosed up to 7.8years post-enrollment (Hazard Ratio [HR]=2.6, p<0.001). In the WHI CT validation population, it classified 8% of women as elevated risk, identifying 31% of cancers diagnosed within 7years of enrollment (HR=4.6, p<0.001).

Conclusion: CA125 and HE4 contributed significantly to a risk prediction classifier combining serum markers with epidemiologic risk factors. The hybrid risk classifier may be useful to identify post-menopausal women who would benefit from timely surgical intervention to prevent epithelial ovarian cancer.

Keywords: CA125; HE4; Ovarian cancer; Risk prediction.

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Figures

Figure 1
Figure 1
Performance and validation of candidate risk classifiers

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