RT Journal Article
SR Electronic
T1 Base rate of ovarian cancer on algorithms in patients with a pelvic mass
JF International Journal of Gynecologic Cancer
JO Int J Gynecol Cancer
FD BMJ Publishing Group Ltd
SP ijgc-2020-001416
DO 10.1136/ijgc-2020-001416
A1 Rolfsen, Anne Lone Denny
A1 Dahl, Alv A
A1 Pripp, Are Hugo
A1 Dørum, Anne
YR 2020
UL http://ijgc.bmj.com/content/early/2020/07/21/ijgc-2020-001416.abstract
AB Objective 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.Methods 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.Results 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%.Conclusion This study demonstrated that the base rate of ovarian cancer in the samples tested was important for the case finding abilities of algorithms.