title: How to differentiate benign from malignant myometrial tumours using MR imaging.
abstract: OBJECTIVE
  To retrospectively evaluate the ability of magnetic resonance imaging (MRI) to differentiate malignant from benign myometrial tumours.
  
  
  METHODS
  Fifty-one women underwent MRI before surgery for evaluation of a solitary myometrial tumour. At histopathology, there were 25 uncertain or malignant mesenchymal tumours and 26 benign leiomyomas. Conventional morphological MRI criteria were recorded in addition to b 1,000 signal intensity and apparent diffusion coefficient (ADC). Odds ratios (OR) were calculated for each criterion. A multivariate analysis was performed to construct an interpretation model.
  
  
  RESULTS
  The significant criteria for prediction of malignancy were high b 1,000 signal intensity (OR = +∞), intermediate T2-weighted signal intensity (OR = +∞), mean ADC (OR = 25.1), patient age (OR = 20.1), intra-tumoral haemorrhage (OR = 21.35), endometrial thickening (OR = 11), T2-weighted signal heterogeneity (OR = 10.2), menopausal status (OR = 9.7), heterogeneous enhancement (OR = 8) and non-myometrial origin on MRI (OR = 4.9). In the recursive partitioning model, using b 1,000 signal intensity, T2 signal intensity, mean ADC, and patient age, the model correctly classified benign and malignant tumours in 47 of the 51 cases (92.4 %).
  
  
  CONCLUSIONS
  We have developed an interpretation model usable in routine practice for myometrial tumours discovered at MRI including T2 signal, b 1,000 signal and ADC measurement.
  
  
  CONCLUSIONS
  • MRI is widely used to differentiate benign from malignant myometrial tumours. • By combining T2-weighted, b 1,000 and ADC features, MRI is 92.4 % accurate. • DWI may limit misdiagnoses of uterine sarcoma as benign leiomyoma. • Patient age is important when considering a solitary myometrial tumour.
authors.affiliation: Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris and Université Pierre et Marie Curie, Paris, France. isabelle.thomassin@tnn.aphp.fr
authors.forename: Isabelle
authors.initials: I
authors.lastname: Thomassin-Naggara
authors.forename: Sophie
authors.initials: S
authors.lastname: Dechoux
authors.forename: Claire
authors.initials: C
authors.lastname: Bonneau
authors.forename: Audrey
authors.initials: A
authors.lastname: Morel
authors.forename: Roman
authors.initials: R
authors.lastname: Rouzier
authors.forename: Marie-France
authors.initials: MF
authors.lastname: Carette
authors.forename: Emile
authors.initials: E
authors.lastname: Daraï
authors.forename: Marc
authors.initials: M
authors.lastname: Bazot
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D004058:Diffusion; D005260:Female; D006801:Humans; D007889:Leiomyoma / Q000175:diagnosis / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015999:Multivariate Analysis; D009215:Myometrium / Q000473:pathology*; D015588:Observer Variation; D016017:Odds Ratio; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014594:Uterine Neoplasms / Q000175:diagnosis* / Q000473:pathology*
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