ACCEPT (ACcuracy, Cost Effectivenss of Prediction models for ovarian Tumors): a study on the cost-effectiveness of risk scoring models for the discrimination between benign or malignant ovarian tumors

OBJECTIVE The aim of the ACCEPT study was to determine which risk stratification strategy is most accurate and cost-effective for distinguishing between benign and malignant ovarian cysts. In addition to diagnostic accuracy, we evaluated costs and the impact on quality of life, anxiety, and cancer-related worry. STUDY DESIGN Prospective multicenter cohort study combined with a systematic review and meta-analysis and a model-based cost-effectiveness analysis.

OBJECTS/UNITS OF ANALYSIS 584 women aged ≥18 years with an ovarian cyst and an indication for surgery, included in 27 Dutch hospitals. Patients were seen at outpatient clinics in general hospitals and were able to understand Dutch. Patients with an evidently benign tumor on ultrasound, clear malignancy, a history of ovarian or other recent malignancy, or a known genetic high-risk for ovarian cancer were excluded.

Data collection included patient-reported outcomes, healthcare utilization, and referral patterns. INTERVENTION Use of predictive models for risk stratification (such as RMI, ADNEX and subjective assessment) to support referral decisions.