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MR imaging of ovarian masses: classification and differential diagnosis.
Foti, Pietro Valerio; Attinà, Giancarlo; Spadola, Saveria; Caltabiano, Rosario; Farina, Renato; Palmucci, Stefano; Zarbo, Giuseppe; Zarbo, Rosario; D'Arrigo, Maria; Milone, Pietro; Ettorre, Giovanni Carlo.
Afiliación
  • Foti PV; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy. pietrofoti@hotmail.com.
  • Attinà G; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy.
  • Spadola S; Department G.F. Ingrassia - Institute of Pathology, University of Catania, Catania, Italy.
  • Caltabiano R; Department G.F. Ingrassia - Institute of Pathology, University of Catania, Catania, Italy.
  • Farina R; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy.
  • Palmucci S; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy.
  • Zarbo G; Department of General Surgery and Medical-Surgical Specialties - Institute of Obstetrics and Ginecology, University of Catania, Catania, Italy.
  • Zarbo R; Department of General Surgery and Medical-Surgical Specialties - Institute of Obstetrics and Ginecology, University of Catania, Catania, Italy.
  • D'Arrigo M; Pathology Unit, University Hospital "Policlinico-Vittorio Emanuele", Catania, Italy.
  • Milone P; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy.
  • Ettorre GC; Radiodiagnostic and Radiotherapy Unit, University Hospital "Policlinico-Vittorio Emanuele", Via Santa Sofia 78, 95123, Catania, Italy.
Insights Imaging ; 7(1): 21-41, 2016 Feb.
Article en En | MEDLINE | ID: mdl-26671276
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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Insights Imaging Año: 2016 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Insights Imaging Año: 2016 Tipo del documento: Article País de afiliación: Italia
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