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J Clin Oncol. 2005 Nov 1;23(31):7919-26.

Prospective study using the risk of ovarian cancer algorithm to screen for ovarian cancer.

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  • 1Department of Gynecological Oncology, Institute of Women's Health, University College London.

Abstract

PURPOSE:

To evaluate prevalence screening in the first prospective trial of a new ovarian cancer screening (OCS) strategy (risk of ovarian cancer or ROC algorithm) on the basis of age and CA125 profile.

PATIENTS AND METHODS:

Postmenopausal women, > or = 50 years were randomly assigned to a control group or screen group. Screening involved serum CA125, interpreted using the ROC algorithm. Participants with normal results returned to annual screening; those with intermediate results had repeat CA125 testing; and those with elevated values underwent transvaginal ultrasound (TVS). Women with abnormal or persistently equivocal TVS were referred for a gynecologic opinion.

RESULTS:

Thirteen thousand five hundred eighty-two women were recruited. Of 6,682 women randomly assigned to screening, 6,532 women underwent the first screen. After the initial CA125, 5,213 women were classified as normal risk, 91 women elevated, and 1,228 women intermediate. On repeat CA125 testing of the latter, a further 53 women were classified as elevated risk. All 144 women with elevated risk had TVS. Sixteen women underwent surgery. Eleven women had benign pathology; one woman had ovarian recurrence of breast cancer; one woman had borderline; and three women had primary invasive epithelial ovarian cancer (EOC). The specificity and positive predictive value (PPV) for primary invasive EOC were 99.8% (95% CI, 99.7 to 99.9) and 19% (95% CI, 4.1 to 45.6), respectively.

CONCLUSION:

An OCS strategy using the ROC algorithm is feasible and can achieve high specificity and PPV in postmenopausal women. It is being used in the United Kingdom Collaborative Trial of Ovarian Cancer Screening and in the United States in both the Cancer Genetics Network and the Gynecology Oncology Group trials of high-risk women.

PMID:
16258091
[PubMed - indexed for MEDLINE]
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