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Objective: To evaluate the performance of the RMI 4 in discriminating benign from malignant ovarian masses.

Study Design: Cross-sectional study.

Setting: Assiut Women Health Hospital- Egypt.

Materials and methods: This was an observational cross-sectional study involving 91 patients at Women's Health Hospital, Assiut University, Egypt during the period between January, 2016 and January, 2017. Women with ovarian masses planned for surgical management were recruited from the outpatient gynecology clinic of the hospital. Risk of malignancy index (RMI 4) was calculated for all study participants. Biopsies obtained from the ovarian masses after surgical intervention were sent to the pathology lab for histopathological examination. The histopathologic diagnosis of the ovarian masses is considered the gold standard for diagnosis.

Results: The mean age of patients in the benign group was 34.83±16.28 years versus 43.43±15.91 in the malignant group. There were 12 postmenopausal patients (15.6%) in the benign group versus 4 postmenopausal patients (28.6%) in the malignant group (p=0.0001). An ultrasound score of 4 was recorded in 85.7% of patients in the malignant group versus only 6.5% in the benign group (p=0.0001). Additionally, tumor size ≥ 7 cm was observed in 85.7% of patients in the malignant group versus 55.8% in the benign group (p=0.0001). The mean value of CA-125 was significantly higher in malignant group than the benign group (142.09±41.50 versus 54.51±32.86 ml, respectively) with p=0.01. RMI 4 had a sensitivity of 75%, specificity of 97.3%, PPV of 85.7%, NPV of 94.8 % and an overall accuracy of 93.4%.

Conclusions: RMI 4 is a simple and reliable tool in the primary evaluation of patients with ovarian masses. It can further be used to discriminate benign from malignant ovarian masses with high sensitivity and accuracy.


Ovarian masses, risk of malignancy index, ovarian cancer, CA-125

Total Pages

9 pages

Financial Disclosure

The authors report no conflict of interest


Copyright © 2018 Mustafa N. Ali, Dina Habib, Ahmed I. Hassanien, Ahmed M. Abbas, and Mohamed H. Makarem

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.