An algorithm for the prediction of benign vs. malignant disease will be constructed using the clinical information, serum biomarkers and mRNA markers. Additional analyses may be performed within and between various histopathological diagnosis sub-groups. The variable selection and algorithm construction will be done using various statistical methods, such as logistic regression, hierarchal clustering, classification and regression trees (CART), ROC curve evaluation, sensitivity/specificity analysis, visual plotting for determination of thresholds, etc. The inputs for evaluation may include continuous variables (e.g. age, ovary and dominant mass dimensions, serum biomarker results, mRNA expression levels, etc.), categorical variables (e.g. age groups, biomarker results by ranges, mRNA expression levels by ranges, etc.), and/or binary variables (e.g. presence or absence of particular risk factors and/or imaging features, age above or below a particular threshold, menopausal status, biomarker results above or below a particular threshold, mRNA expression levels above or below a particular threshold, etc.). A threshold for the resulting algorithm(s) output to differentiate between benign and malignant disease (or a subgroup thereof, such as epithelial ovarian cancer patients only) will be selected to optimize the sensitivity at a set specificity (e.g. maximize sensitivity at a minimum specificity level of >80%).