Breast MR imaging: interpretation model

Radiology. 1997 Mar;202(3):833-41. doi: 10.1148/radiology.202.3.9051042.


Purpose: To develop an interpretation model based on architectural features of suspicious breast findings on magnetic resonance (MR) images.

Materials and methods: One hundred ninety-two patients with mammographically visible or palpable findings underwent T1- and fat-saturated T2-weighted spin-echo and contrast agent-enhanced fat-saturated gradient-echo MR imaging. Patients underwent subsequent excisional biopsy for histopathologic confirmation. An interpretation model was constructed by using 98 cases and was tested prospectively and expanded by using 94 different cases. Sensitivity, specificity, predictive values, and receiver operating characteristic curves were computed for all models.

Results: Individual features with high predictive values were MR visibility, enhancement degree and pattern, focal mass border characteristics, and focal mass internal septations. Feature combinations with high negative predictive values for malignancy were absence of an MR-visible abnormality, focal masses with smooth borders, lobulated or irregular masses with nonenhancing internal septations, and focal masses with no (or minimal) enhancement. The validated- and revised-model performance characteristics were, respectively, as follows: sensitivity, 100% and 96%; specificity, 69% and 79%; positive predictive value, 75% and 76%; negative predictive value, 100% and 97%; and overall accuracy, 83% and 86%.

Conclusion: An interpretation model that incorporates breast MR architectural features can achieve high sensitivity and improve specificity for diagnosing breast cancer.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Biopsy
  • Breast / pathology*
  • Breast Neoplasms / diagnosis*
  • Decision Trees
  • Female
  • Humans
  • Magnetic Resonance Imaging*
  • Middle Aged
  • Predictive Value of Tests
  • Prospective Studies
  • ROC Curve
  • Sensitivity and Specificity