Background The cancer antigen 125 (CA125) immunoassay (IA) does not distinguish epithelial ovarian cancer (EOC) from benign disease with the sensitivity needed in clinical practice. In recent studies, glycoforms of CA125 have shown potential as biomarkers in EOC. Here, we assessed the diagnostic abilities of two recently developed CA125 glycoform assays for patients with a pelvic mass. Detailed analysis was further conducted for postmenopausal patients with marginally elevated conventionally measured CA125 levels, as this subgroup presents a diagnostic challenge in the clinical setting. Methods Our study population contained 549 patients diagnosed with EOC, benign ovarian tumors, and endometriosis. Of these, 288 patients were postmenopausal, and 98 of them presented with marginally elevated serum levels of conventionally measured CA125 at diagnosis. Preoperative serum levels of conventionally measured CA125 and its glycoforms (CA125-MGL and CA125-STn) were determined. Results The CA125-STn assay identified EOC significantly better than the conventional CA125-IA in postmenopausal patients (85% vs. 74% sensitivity at a fixed specificity of 90%, P = 0.0009). Further, both glycoform assays had superior AUCs compared to the conventional CA125-IA in postmenopausal patients with marginally elevated CA125. Importantly, the glycoform assays reduced the false positive rate of the conventional CA125-IA. Conclusions The results indicate that the CA125 glycoform assays markedly improve the performance of the conventional CA125-IA in the differential diagnosis of pelvic masses. This result is especially valuable when CA125 is marginally elevated.

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.