Serum miRNA improves the accuracy of a multivariate index assay for triage of an adnexal mass

To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81–0.95) and external validation sets (AUC = 0.95; 95% CI 0.90–0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass. •Serum miRNA and protein biomarkers are complementary in estimating the risk of malignancy of an ovarian mass.•An integrated model with proteins, miRNA, and metadata offers the most accurate classification performance.•The addition of miRNA improves sensitivity and specificity for identification of early-stage ovarian cancer.