Development of an optimized diagnostic algorithm for differentiating benign ovarian cysts and ovarian cancer

​A prospective, multicenter study with the aim of developing an optimized diagnostic algorithm for differentiating benign ovarian cysts and ovarian cancer

A research project by Nikoline Schou Karlsen

Introduction: It is of utmost importance to correctly distinguish ovarian cancer (OC) from the commonly occurring benign ovarian cysts. Patients with OC require early and highly specialized handling, whereas patients with benign ovarian cysts can be managed at the local gynecologic department or gynecology practice. It can be challenging to triage patients with ovarian cysts with the diagnostic tools available. Optimized risk assessment tools are needed for differentiating ovarian cysts in order to minimize incorrect triaging.

Aims: The aim is to develop and validate an optimized diagnostic algorithm for triaging ovarian cysts. The DOC study evaluates 1) the Copenhagen Index (CPH-I) based on blood-markers HE4, CA125 and age, 2) systematic ultrasound imaging according to International Ovarian Tumor Analysis (IOTA), and 3) symptoms in women with unknown ovarian cysts.

Methods: Around 2000 women with ovarian cysts of unknown histology are expectedly enrolled in the multi-center DOC study at gynecologic departments and gynecology practices. Data is prospectively collected as a part of the routine examinations and coupled with information from patient files and the Danish Gynecological Cancer Database (DGCD). Required study approvals are obtained. The DOC study has been tested and established at Rigshospitalet and Hillerød Hospital and is currently established in the remaining institutions in the Capitol Region of Denmark.

Perspectives: An optimized diagnostic algorithm will ensure more accurate referral and hence optimized treatment and use of resources.


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