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Open Access Research

Evaluation of primary adnexal masses by 3T MRI: categorization with conventional MR imaging and diffusion-weighted imaging

He Zhang1, Guo-Fu Zhang2, Zhi-Yan He1*, Zheng-Yu Li1, Ming Zhu1 and Gui-Xiang Zhang1

Author Affiliations

1 Department of Radiology, Shanghai First People’s Hospital, Medical College, Shanghai Jiaotong University, No. 100, Hai Ning Road, Shanghai, 200080, China

2 Department of Radiology,Obstetrics and Gynecology Hospital, The Affiliated hospital of Fudan University, No. 419, Fang xie Road, Shanghai, 200011, China

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Journal of Ovarian Research 2012, 5:33  doi:10.1186/1757-2215-5-33

Published: 14 November 2012

Abstract

Background

To investigate the 3.0-Tesla (3 T) magnetic resonance imaging (MRI) characteristics of primary adnexal lesions for discriminating benign from malignant lesions.

Methods

One hundred thirty-nine patients with pathologically proven primary adnexal masses referred for 3 T MRI assessment preoperatively were included. Baseline characteristics, components, and conventional MRI and diffusion-weighted imaging (DWI-MRI) signals were recorded and compared.

Results

There were 22 ovarian cysts, 33 endometriomas, 43 benign tumors and 42 malignant tumors. When ovarian cyst and endometrioma were excluded, there were no significant differences in patients’ age between benign and malignant tumor (P = 0.235). There were no significant differences (P = 0.606) in the conventional MRI signals and significant difference (P = 0.008) in DWI-MRI signal between the non-malignant and malignant lesions. There was a significant difference (P = 0.000) in the apparent diffusion coefficient values (ADCs) between the non-malignant and malignant lesions.

Conclusions

3 T MRI categorized the characteristics of primary adnexal lesions. Conventional MRI signals were not useful for characterizing between benign and malignant lesions. DWI-MRI and ADCs were helpful for distinguishing malignant from benign ovarian lesions.

Keywords:
Magnetic resonance images; Diffusion weighted imaging; 3.0 Tesla; Ovary diseases