MR imaging of ovarian masses: classification and differential diagnosis

Abstract

Objective

We propose a Magnetic Resonance Imaging (MRI) guided approach to differential diagnosis of ovarian tumours based on morphological appearance.

Background

Characterization of ovarian lesions is of great importance in order to plan adequate therapeutic procedures, and may influence patient's management. Optimal assessment of adnexal masses requires a multidisciplinary approach, based on physical examination, laboratory tests and imaging techniques. Primary ovarian tumours can be classified into three main categories according to tumour origin: epithelial, germ cell and sex cord-stromal tumours. Ovarian neoplasms may be benign, borderline or malignant. Using an imaging-guided approach based on morphological appearance, we classified adnexal masses into four main groups: unilocular cyst, multilocular cyst, cystic and solid, predominantly solid. We describe MR signal intensity features and enhancement behaviour of ovarian lesions using pathologically proven examples from our institution.

Conclusion

MRI is an essential problem-solving tool to determine the site of origin of a pelvic mass, to characterize an adnexal mass, and to detect local invasion. The main advantages of MRI are the high contrast resolution and lack of ionizing radiation exposure. Although different pathological conditions may show similar radiologic manifestations, radiologists should be aware of MRI features of ovarian lesions that may orientate differential diagnosis.

Teaching Points

• Diagnostic imaging plays a crucial role in detection, characterization and staging of adnexal masses.

• Characterization of an ovarian lesion may influence patient's management.

• Different pathological conditions may have similar radiologic manifestations.

• Non-neoplastic lesions should always be taken into consideration.

from #ORL-AlexandrosSfakianakis via simeraentaxei on Inoreader http://ift.tt/1UAlA9h
via IFTTT

from #Med Blogs by Alexandros G.Sfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1TRgMva
via IFTTT