Base rate of ovarian cancer on algorithms in patients with a pelvic mass.

International Journal of Gynecological Cancer : Official Journal of the International Gynecological Cancer Society
Anne Lone Denny RolfsenAnne Dørum

Abstract

Algorithms have been developed to identify ovarian cancer in women with a pelvic mass. The aim of this study was to determine how the base rates of ovarian cancer influence the case finding abilities of recently developed algorithms applicable to pelvic tumors. We used three ovarian cancer algorithms and the principle of Bayes' theorem for risk estimation. First, we evaluated the case finding abilities of the Risk of Malignancy Algorithm, the Rajavithi-Ovarian Predictive Score, and the Copenhagen Index in a prospectively collected sample at Oslo University Hospital of 227 postmenopausal women with a 74% base rate of ovarian cancer. Second, we examined the case finding abilities of the Risk of Malignancy Algorithm in three published studies with different base rates of ovarian cancer. We applied Bayes' theorem in these examinations. In the Oslo sample, all three algorithms functioned poorly as case finders for ovarian cancer. When the base rate changed from 8.2% to 43.8% in the three studies using the Risk of Malignancy Algorithm, the proportion of false negative ovarian cancer diagnoses increased from 1.2% to 3.4%, and the number of false positive diagnosis increased from 4.6% to 14.2%. This study demonstrated that the base rat...Continue Reading

References

May 10, 2005·BMJ : British Medical Journal·Christopher J GillChristopher H Schmid
Sep 12, 2015·Tidsskrift for den Norske lægeforening : tidsskrift for praktisk medicin, ny række·Brage Brakedal
Mar 29, 2019·Journal of Ovarian Research·Vincent DochezGuillaume Ducarme

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