Diagnostic value of risk of malignancy index in the clinical evaluation of ovarian mass
- Ali Huwidi
- Afaf Abobrege
- Mourad Assidi
- Abdelbaset Buhmeida
- Eramah Ermiah
Affiliations: Department of Gynaecology, National Cancer Institute, Misurata University, Misurata 051, Libya, Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Makkah 21589, Saudi Arabia, Medical Research Unit, National Cancer Institute, Misurata 051, Libya
- Published online on: May 30, 2022 https://doi.org/10.3892/mco.2022.2551
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In the present study, the Risk Malignancy Index (RMI) was calculated based on menopausal status, ultrasound (US) findings and serum biological cancer antigen 125 (CA‑125) levels as a scoring system in Libyan females with ovarian masses (OMs) to differentiate between benign and malignant tumors. A total of 51 females with OMs referred to the Gynaecology Department of the National Cancer Institute in Misurata (Libya) between January 2019 and December 2020 were retrospectively reviewed for diagnostic testing. Clinicopathological and demographic data were obtained from patient records. A cut‑off point of RMI=200 was used to differentiate between benign and malignant tumors. The mean age of the patients was 47 years (range, 19‑90 years) and 60% of the patients were premenopausal. Examination of the four RMI indices and disease status indicated that the association with the US score (P<0.0001) and with CA‑125 (P=0.017) was highly significant. However, the age at diagnosis and menopausal status did not have any significant association with the disease status. The RMI with a cut‑off point of 200 had a sensitivity and specificity of 87.5 and 90.7%, respectively, and a positive and negative predictive value of 63.6 and 97.5%, respectively. The association between the RMI and disease status was highly significant (P<0.0001). In conclusion, the RMI appears to be a reliable, simple and cost‑effective tool for clinical differentiation between benign and malignant OMs. This may help to improve the optimal diagnosis and planning of an individualized treatment strategy. However, given the small sample size of the cohort, further validation using larger cohorts in other settings is recommended.