Comparing Four Different Risk Malignancy Indices in Differentiating Benign and Malignant Ovarian Masses

Author:

Mundhra Rajlaxmi1,Bahadur Anupama1,Kashibhatla Jyotshna1,Kishore Sanjeev2,Chaturvedi Jaya1

Affiliation:

1. Department of Obstetrics and Gynecology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India

2. Department of Pathology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India

Abstract

ABSTRACT Background: Accurate prediction of ovarian masses preoperatively is crucial for optimal management of ovarian cancers. Objective: The objective of this study was to identify the risk of malignancy index (RMI) incorporating menopausal status, serum carbohydrate antigen 125 levels, and imaging findings for presurgical differentiation of benign from malignant ovarian masses and to evaluate the diagnostic ability of four different RMIs. Materials and Methods: Women presenting with ovarian masses from August 2018 to January 2020 were evaluated preoperatively with detailed history, examination, imaging, and tumor markers. RMI 1–4 was calculated for all patients. Evaluation of the diagnostic utility of four different RMIs for preoperative identification of malignancy was based on the increment of the area under the receiver operating characteristic curve. Histopathological diagnosis was used as the gold standard test. Results: One hundred and twenty-one patients fulfilling the eligibility criteria were enrolled in this study. Benign tumors constituted 61 (50.4%) out of 121 cases, followed by malignant tumors and borderline tumors constituting 49 (40.49%) cases and 11 (9.09%) cases, respectively. The sensitivity of RMIs 1, 2, 3, and 4 was 77.0%, 63%, 77.0%, and 77.0%, respectively, and the specificity was 84%, 86%, 77%, and 71%, respectively. The RMI 2 had higher specificity at predicting malignancy than other RMIs while diagnostic accuracy was highest in RMI 1. Conclusion: The RMI method is a simple and cost-effective technique in preoperative differentiation of ovarian masses.

Publisher

Medknow

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