Performance of ovarian malignancy algorithm in predicting pelvic mass in patients at risk of ovarian cancer / 中国肿瘤临床

ABSTRACT

Objective:This study aims to evaluate the performance of the predictive model risk of ovarian malignancy algorithm (ROMA) using serum human epididymis protein 4 (HE4) and CA125 for discriminating benign from malignant pelvic masses. Meth-odsThe electrochemical luminescence method was carried out before and after the menopause prediction model (ROMA) to detect se-rum HE4 and CA125 levels of patients admitted in hospital for surgery of pelvic masses or ovarian cyst. Patients were classified into high-and low-risk groups to evaluate the value of the applied prediction model. Results:Of the 1683 patients evaluated, 1448 had pel-vic benign disease and 235 had pelvic malignancy, including 106 EOC. In the pathological diagnosis of patients with benign pelvic masses, 1356 cases were classified into the low-risk group with a specificity of 93.6%. A total of 121 cases of pelvic malignant tumors with sensitivity of 80.7%and 20 cases of border ovarian epithelial tumor with sensitivity of 28.2%were categorized in the high-risk group. EOC cases (93) were also classified in the high-risk group, with sensitivity of 87.7%. The cases that were not classified in the high-risk group included 2 of mucous adenocarcinoma and 11 of clear cell carcinoma. Five cases of non-epithelial ovarian malignant tu-mor patients were classified into the high-risk group with a sensitivity of 38.5%. Thirty-five cases of ovarian malignant tumor patients with a sensitivity of 85.3% and one case of metastatic ovarian cancer patient with a sensitivity of 25.0% were categorized in the high-risk group. Conclusions: ROMA can be successfully applied in pelvic malignant tumor patients to classify them into high-risk group. Most EOC patients belong to the high-risk group. ROMA can be used in patients with malignant EOC, especially during prelimi-nary screening.