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Algorithm Identifies Women at Risk of Ovarian Cancer

Algorithm Identifies Women at Risk of Ovarian Cancer

PHILADELPHIA--New computer software is using an investigational
algorithm to translate serial CA 125 values and other risk factors
into a single number showing a postmenopausal women's risk of
developing ovarian cancer, Steven J. Skates, PhD, assistant professor
of medicine and biostatistics, Harvard Medical School, said at
his American Society of Clinical Oncology poster presentation.

22,000 Postmenopausal Women

Dr. Skates and his colleagues applied the Risk of Ovarian Cancer
(ROC) algorithm to blood samples collected from more than 22,000
postmenopausal women in an ovarian cancer screening study conducted
by Ian Jacobs, MD, of St. Bartholomew's Hospital, London.

The results showed a sensitivity of 86% and specificity of 99.7%
for the ROC algorithm in identifying women at high risk. These
figures combine to give a positive predictive value above 10%,
which exceeds the minimum requirement for an ovarian cancer screening
test as suggested in the medical literature, he said.

Dr. Skates, who developed the algorithm along with Robert C. Knapp,
MD, professor emeritus, Harvard Medical School, estimates that
12 to 16 of every 100 women identified by the program as high
risk will, in fact, have ovarian cancer. That represents at least
a sixfold improvement over the 2 in 100 women who will have the
disease when identified using only a single elevated CA 125 level.

To calculate risk, the algorithm uses serial CA 125 assay values,
changes in those levels over time, the woman's age, and assay
variability. The algorithm triages women into one of three risk
categories: normal (ROC level of 0.05% and below); intermediate
(0.05% to 4%); and elevated (above 4%).

Those in the normal category would continue annual CA 125 screening;
intermediate scores would indicate the need for a repeat test
in 1 to 6 months; and high-risk women would be referred for further
diagnostic evaluation such as ultrasound.

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