Several predictive models and scoring systems have been developed to differentiate between benign and malignant ovarian masses, in order to guide effective management. These models use combinations of patient characteristics, ultrasound markers, and biochemical markers.
The aim of this study was to describe, compare, and prioritize, according to their strengths and qualities, all the adnexal prediction models.
This was a state-of-the-art review, synthesizing the findings of the current published literature on the available prediction models of adnexal masses.
The existing models include subjective assessment by expert sonographers, the International Ovarian Tumor Analysis models (logistic regression models 1 and 2, Simple Rules, 3-step strategy, and ADNEX [Assessment of Different NEoplasias in the adneXa] model), the Risk of Malignancy Index, the Risk of Malignancy Ovarian Algorithm, the Gynecologic Imaging Reporting and Data System, and the Ovarian-Adnexal Reporting and Data System. Overall, subjective assessment appears to be superior to all prediction models. However, the International Ovarian Tumor Analysis models are probably the best available methods for nonexpert examiners. The Ovarian-Adnexal Reporting and Data System is an international approach that incorporates both the common European and North American approaches, but still needs to be validated.
Many prediction models exist for the assessment of adnexal masses. The adoption of a particular model is based on local guidelines, as well as sonographer's experience. The safety of expectant management of adnexal masses with benign ultrasound morphology is still under investigation.
Obstetricians and gynecologists, family physicians.
After participating in this activity, the learner should be better able to explain all the aspects on the available prediction models of adnexal masses; assess the available prediction models of adnexal masses in terms of their accuracy; and describe the sonographic findings of an adnexal mass and estimate the relevant malignancy risk.