The characteristic ultrasound features of specific types of ovarian pathology (Review)

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Abstract

Characterizing ovarian masses enables patients with malignancy to be appropriately triaged for treatment by subspecialist gynecological oncologists, which has been shown to optimize care and improve survival. Furthermore, correctly classifying benign masses facilitates the selection of patients with ovarian pathology that may either not require intervention, or be suitable for minimal access surgery if intervention is required. However, predicting whether a mass is benign or malignant is not the only clinically relevant information that we need to know before deciding on appropriate treatment. Knowing the specific histology of a mass is becoming of increasing importance as management options become more tailored to the individual patient......

 1. Introduction

The characterization of ovarian masses and distinguishing between benign and malignant pathology is important both to decrease unnecessary anxiety and enable decisions regarding optimal treatment. Benign pathology may be best treated conservatively or in a general gynecology unit using a minimal access approach. Conversely, suspected malignant masses should be referred to specialized units for further management. Thus prior knowledge of the nature of ovarian masses is essential not only for the patient but in order to organize clinical services in terms of planning, costs and overall management (1).

Transvaginal ultrasonography (TVS) is the most commonly employed imaging modality for the assessment of adnexal masses, and a number of prediction models have been created to maximize its predictive capability. In many countries the risk of malignancy index (RMI) (2) which combines ultrasound features, serum CA125 levels and the menopausal status of the patient is still used to characterize ovarian pathology. However, more recently logistic regression models and simple rules created by the International Ovarian Tumor Analysis (IOTA) group have been shown to perform better than the RMI (3–7).......