Comparison of ADNEX model, simple rules risk model and risk of malignancy index in diagnosis of benign and malignant ovarian tumors
10.13929/j.1003-3289.201805166
- Author:
Ping HE
1
Author Information
1. Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University
- Publication Type:Journal Article
- Keywords:
ADNEX model;
Ovarian neoplasms;
Risk of malignancy index;
Simple rules risk model;
Ultrasonography
- From:
Chinese Journal of Medical Imaging Technology
2019;35(1):104-107
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare the value of ADNEX model, simple rules risk model and the risk of malignancy index (RMI) in diagnosis of benign and malignant ovarian tumors. Methods The preoperative ultrasonic images of 286 patients with ovarian tumors were retrospectively analyzed. ADNEX model, simple rules risk model and RMI were used to differentiate benign and malignant ovarian tumors. Taken histopathological results after surgery as golden standards, the sensitivity and specificity were calculated and compared among 3 methods. ROC curve was used to obtain the area under the curves. Results Among 286 ovarian tumors, 142 were benign and 144 were malignant. The sensitivity of ADNEX model, simple rules risk model and RMI was 83.33% (120/144), 80.56% (116/144) and 65.97% (95/144), respectively, while the specificity was 89.44% (127/142), 92.96% (132/142) and 90.14% (128/142), respectively. There was no statistical difference of sensitivity nor specificity between ADNEX model and simple rules risk model (χ2=0.352, 1.784, P=0.554, 0.182). The sensitivity of ADNEX model and simple rules risk model was higher than that of RMI (χ2=16.691, 7.533, respectively, both P<0.001), while there was no statistical difference of specificity (χ2=0, 0.561, P=1, 0.454). The AUC of ADNEX model, simple rules risk model and RMI was 0.864, 0.868 and 0.788, respectively (all P<0.001). Conclusion ADNEX model and simple rules risk model are better than RMI in differentiating benign and malignant ovarian tumors.
