Characterization of adnexal masses to identify patients with malignant ovarian tumors preoperatively for referral to a cancer center for treatment has been extensively studied. A simple algorithm called “risk of malignancy index” (RMI) reported by Jacobs incorporated the serum CA125 level, menopausal status, and ultrasound morphologic features. This algorithm has subsequently been tested on retrospective and prospective data with encouraging results. However, these studies did not include cases that had had both their serum CA125 measurements and ultrasound examinations from a diverse range of laboratories and sonographers. The purpose of this study was to determine the effectiveness of the RMI algorithm for identifying cases of ovarian malignancy presenting at cancer units for subsequent referral to a cancer center. All cases of suspected ovarian malignancy referred to the Northern Gynaecological Oncology Centre (NGOC) during an 18-month period were identified from the NGOC database. A case note review was performed, and the following data were extracted: patient demographics, the referring physician and the operating surgeon, ultrasound morphology, serum CA125 levels, and menopausal status. All patients had their ultrasound performed by sonographers at the peripheral unit according to local protocols. A total of 182 patients with a pelvic mass were referred to the center for surgery. A total of 24% patients had benign tumors, 6% had tumors of borderline malignancy, and 70% had invasive tumors. A total of 145 cases had an RMI >200; 125 of these had ovarian or peritoneal cancers. An RMI >200 had a sensitivity of 88.5% for diagnosing invasive lesions. The overall sensitivity of this algorithm for diagnosing all borderline, invasive ovarian, or primary peritoneal lesions was 87.4%, and the positive predictive value was 86.8%. Our data confirm the effectiveness of the RMI algorithm in clinical practice for the identification and subsequent referral to cancer centers of cases of potential ovarian malignancy. We therefore recommend its continued use.
- centralization of treatment
- ovarian cancer
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