Risk-reducing salpingectomy with delayed oophorectomy to prevent ovarian cancer in women with an increased inherited risk: insights into an alternative strategy.

Epithelial ovarian cancer (EOC) is the most lethal type of gynaecological cancer, due to lack of effective screening possibilities and because the disease tends to metastasize before onset of symptoms. Women with an increased inherited risk for EOC are advised to undergo a risk-reducing salpingo-oophorectomy (RRSO), which decreases their EOC risk by 96% when performed within guideline ages. However, it also induces premature menopause, which has harmful consequences. There is compelling evidence that the majority of EOCs originate in the fallopian tube. Therefore, a risk-reducing salpingectomy with delayed oophorectomy (RRS with DO) has gained interest as an alternative strategy. Previous studies have shown that this alternative strategy has a positive effect on menopause-related quality of life and sexual health when compared to the standard RRSO. It is hypothesized that the alternative strategy is non-inferior to the standard RRSO with respect to oncological safety (EOC incidence). Three prospective studies are currently including patients to compare the safety and/or quality of life of the two distinct strategies. In this article we discuss the background, opportunities, and challenges of the current and alternative strategy.

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review.

OBJECTIVES: Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS: We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS: In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION: Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.