Abstract
ObjectiveTo develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.Study designWe performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the prediction model.ResultsOvarian cancer including borderline tumours was found in 241 (26.6%) patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87–0.94). DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.ConclusionWe confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.
Highlights
In western countries, epithelial ovarian cancer (EOC) is the second most common cancer of the female reproductive system and accountable for as many deaths as all other gynaecological cancers combined [1]
In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram
Decision curve analysis (DCA) revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none
Summary
Epithelial ovarian cancer (EOC) is the second most common cancer of the female reproductive system and accountable for as many deaths as all other gynaecological cancers combined [1]. Detection and treatment in high-volume centres by gynaecologic oncology surgeons is known to improve survival outcome [2, 3]. While adnexal masses are common sonographic findings in women of all ages, approximately 75% of tumours are found to be benign [4, 5]. Identifying those with a high risk of having ovarian cancer often poses a daily clinical challenge. It is of major importance to accurately differentiate between benign and malignant adnexal masses in order to avoid unnecessary surgical procedures and to deliver optimal care to those who are likely to harbour an ovarian cancer
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