Endometrial polyps: MR imaging features and distinction from endometrial carcinoma

Radiology. 2000 Jan;214(1):47-52. doi: 10.1148/radiology.214.1.r00ja3647.

Abstract

Purpose: To determine the magnetic resonance (MR) imaging characteristics of endometrial polyps and the accuracy of MR imaging in distinguishing endometrial polyps from endometrial carcinomas in a case-control study.

Materials and methods: Cross-referencing pathology records with MR studies from two institutions disclosed 35 patients with surgically proved endometrial polyp or carcinoma after controlling for tumor size. All MR examinations were performed at 1.5 T with T2-weighted fast spin-echo sequences in multiple planes. Three independent readers blinded to histologic diagnoses and clinical data scored each image for the presence of several defined findings.

Results: A central fibrous core (low signal intensity on T2-weighted images) and intratumoral cysts (high signal intensity on T2-weighted images) were seen more frequently in endometrial polyps than in carcinomas; myometrial invasion and necrosis showed high predictive value for carcinomas. The readers' responses showed a mean sensitivity of 79%, specificity of 89%, accuracy of 86%, positive predictive value of 82%, and negative predictive value of 88% for diagnosis of carcinoma. The mean area under the receiver operating characteristic curve for the three readers was 0.87 for the diagnosis of carcinoma.

Conclusion: MR images can help to distinguish most polyps from endometrial carcinomas on the basis of morphologic features. Accuracy does not appear to be sufficient to obviate biopsy, partly because carcinomas and polyps frequently coexist.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Case-Control Studies
  • Diagnosis, Differential
  • Endometrial Neoplasms / diagnosis*
  • Endometrium / pathology
  • Female
  • Humans
  • Magnetic Resonance Imaging*
  • Middle Aged
  • Polyps / diagnosis*
  • Predictive Value of Tests
  • ROC Curve