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©2013 Baishideng. All rights reserved.
World J Radiol. Mar 28, 2013; 5(3): 113-125
Published online Mar 28, 2013. doi: 10.4329/wjr.v5.i3.113
Published online Mar 28, 2013. doi: 10.4329/wjr.v5.i3.113
Multimodality imaging of ovarian cystic lesions: Review with an imaging based algorithmic approach
Ashish P Wasnik, Joel F Platt, Department of Radiology, University of Michigan Health System, Ann Arbor, MI 48105, United States
Christine O Menias, Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
Usha R Lalchandani, Department of Radiology, Grant Medical College, Mumbai 400012, India
Deepak G Bedi, Khaled M Elsayes, Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
Author contributions: Wasnik AP and Elsayes KM designed, wrote and researched the paper; Menias CO and Lalchandani UR searched the literature and edited the images; Platt JF and Bedi DG checked and edited the paper.
Correspondence to: Ashish P Wasnik, MD, Department of Radiology, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI 48105, United States. drashish_w@yahoo.com
Telephone: +1-734-2329048 Fax: +1-734-9360013
Received: June 4, 2012
Revised: August 16, 2012
Accepted: January 31, 2013
Published online: March 28, 2013
Revised: August 16, 2012
Accepted: January 31, 2013
Published online: March 28, 2013
Abstract
Ovarian cystic masses include a spectrum of benign, borderline and high grade malignant neoplasms. Imaging plays a crucial role in characterization and pretreatment planning of incidentally detected or suspected adnexal masses, as diagnosis of ovarian malignancy at an early stage is correlated with a better prognosis. Knowledge of differential diagnosis, imaging features, management trends and an algorithmic approach of such lesions is important for optimal clinical management. This article illustrates a multi-modality approach in the diagnosis of a spectrum of ovarian cystic masses and also proposes an algorithmic approach for the diagnosis of these lesions.
Keywords: Ovarian; Neoplasm; Ultrasound; Computed tomography; Magnetic resonance imaging