ArticleInternational journal of gynecological cancer : official journal of the International Gynecological Cancer Society2020
Base rate of ovarian cancer on algorithms in patients with a pelvic mass.
Article in International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 2 citations in OpenAlex.
- Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study.Cancers · 2022Article
- Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer.Frontiers in oncology · 2022Article
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4 authors at 1 institution in 1 country.
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Abstract
objectiveAlgorithms 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.
methodsFirst, 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.
resultsIn 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%.
conclusionThis study demonstrated that the base rate of ovarian cancer in the samples tested was important for the case finding abilities of algorithms.
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