The Egyptian Journal of Radiology and Nuclear Medicine (Nov 2019)
The diagnostic efficacy of Gynecology Imaging Reporting and Data System (GI-RADS): single-center prospective cross-sectional study
Abstract
Abstract Background To assess the validity and accuracy of GI-RADS classification in the prediction of malignancy and in triaging the management protocol in ovarian lesions. Results One hundred fifty-six ovarian lesions were detected in the examined 116 women. The prevalence of malignant tumors was 44%. Overall GI-RADS classification rates were as follows: 41 cases (26.3%) were classified as GI-RADS 1, 26 cases (16 .7%) as GI-RADS 2, 34 cases (21.8%) as GI-RADS 3, 14 cases (8.9%) as GI-RADS 4, and 41 cases (26.3%) as GI-RADS 5. No follow-up was done in GI-RADS 1 patients. A final diagnosis of all GI-RADS 2 ovarian masses such as functional cyst (n = 10), hemorrhagic cysts (n = 8), corpus luteal cysts (n = 6), and some GI-RADS 3 as simple cysts (n = 10) was made by spontaneous resolution of these masses at follow-up after 6 weeks. Fifteen cases of GI-RADS 3 as mature teratoma, serous and mucinous cystadenoma, endometrioma, and ovarian torsion and all GI-RADS 4 and 5 underwent laparoscopic or surgical removal of the ovarian mass with histopathological examination. The diagnostic performance of the GI-RADS in predicting the risk of malignancy in ovarian masses was as follows: 98.11% sensitivity, 95.15% specificity, 91.2% positive predictive value (PPV), 99.2% negative predictive value (NPV), and 20.2 positive likelihood ratio, and the overall accuracy was 96.2% (area under receiver operating curve (AUC) = 0.96, P < 0.001). Conclusion GI-RADS classification performs well as a reporting system of the ovarian masses with high diagnostic performance in the prediction of malignancy, and it seems to be a helpful tool in triaging management in patients with ovarian masses. Trial registration The trial was registered in the US National Library of Medicine, under clinical trial number NCT03175991. Also, the ethical committee approval number of the Faculty of Medicine, Assiut University, was 17100016 on February 28, 2017.