Improved Detection Rate of Ovarian Cancer Using a 2-Step Triage Model of the Risk of Malignancy Index and Expert Sonography in an Outpatient Screening Setting

    loading  Checking for direct PDF access through Ovid

Abstract

Objective

Preoperative assessment of adnexal masses with ultrasound has been shown to be time-, cost-effective, and specific. When used in combination with the menopausal status and the tumor marker CA125, the risk of malignancy index (RMI) can be calculated, allowing appropriate preoperative triage of patients to a gynecologist or a gynecological oncologist. Moreover, it allows for accurate planning of the required surgical procedure (laparoscopy vs laparotomy).

Methods

A large general gynecologic ultrasonic database retrospectively identified 5218 patients for a 14-year period who presented to the outpatient clinic with an adnexal mass. Additional data (menopausal status, histology, CA125 values) were available in 1108 of these patients. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The results were then compared with previously published data from a large Australian gynecological cancer center (GCC, n = 204).

Results

With the use of an RMI cutoff of 200, malignant ovarian tumors were correctly triaged to a gynecologic oncologist in 123 of 172 cases, leading to a sensitivity of 72% and specificity of 92% in our general outpatient clinic population compared with a sensitivity of 84% and a specificity of 77% in the GCC high-risk population. The negative predictive value was 95% compared with only 85% in the GCC cohort. We hypothesize that improvement of the overall detection rate of malignancy could be improved from 72% to 85% using a 2-step model, referring patients with an ultrasonic score of 3 to an experienced sonographer who uses pattern recognition.

Conclusions

The RMI is an easy and reliable tool for the accurate triage of adnexal masses. Its value is higher in an unselected gynecological outpatient setting. Our proposed 2-step model including expert pattern recognition could influence particularly the detection rate in borderline and early-stage ovarian cancers and overcome the limitations of the tumor marker CA125.

Related Topics

    loading  Loading Related Articles