European Radiology

, Volume 21, Issue 8, pp 1609–1617 | Cite as

Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics

  • Wendy B. DeMartiniEmail author
  • Brenda F. Kurland
  • Robert L. Gutierrez
  • C. Craig Blackmore
  • Sue Peacock
  • Constance D. Lehman



To predict the probability of malignancy for MRI-detected breast lesions with a multivariate model incorporating patient and lesion characteristics.


Retrospective review of 2565 breast MR examinations from 1/03–11/06. BI-RADS 3, 4 and 5 lesions initially detected on MRI for new cancer or high-risk screening were included and outcomes determined by imaging, biopsy or tumor registry linkage. Variables were indication for MRI, age, lesion size, BI-RADS lesion type and kinetics. Associations with malignancy were assessed using generalized estimating equations and lesion probabilities of malignancy were calculated.


855 lesions (155 malignant, 700 benign) were included. Strongest associations with malignancy were for kinetics (washout versus persistent; OR 4.2, 95% CI 2.5–7.1) and clinical indication (new cancer versus high-risk screening; OR 3.0, 95% CI 1.7–5.1). Also significant were age > = 50 years, size > = 10 mm and lesion-type mass. The most predictive model (AUC 0.70) incorporated indication, size and kinetics. The highest probability of malignancy (41.1%) was for lesions on MRI for new cancer, > = 10 mm with washout. The lowest (1.2%) was for lesions on high-risk screening, <10 mm with persistent kinetics.


A multivariate model shows promise as a decision support tool in predicting malignancy for MRI-detected breast lesions.


Breast MRI Kinetics Multivariate analysis Biopsy 



This research was supported by the GE-Radiology Research Academic Fellowship (GERRAF) Program Sponsored by the Association of University Radiologists (AUR).

Support was also provided by a National Cancer Institute (NCI) Cancer Center Support Grant for biostatistics as a shared resource (P30 CA015704). This paper was presented at the Radiological Society of North America (RSNA) 2008 Annual Meeting.


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Copyright information

© European Society of Radiology 2011

Authors and Affiliations

  • Wendy B. DeMartini
    • 1
    • 2
    Email author
  • Brenda F. Kurland
    • 3
    • 5
  • Robert L. Gutierrez
    • 1
    • 2
  • C. Craig Blackmore
    • 4
  • Sue Peacock
    • 1
    • 2
  • Constance D. Lehman
    • 1
    • 2
  1. 1.Department of RadiologyUniversity of Washington Medical CenterSeattleUSA
  2. 2.Breast imagingSeattle Cancer Care AllianceSeattleUSA
  3. 3.Clinical StaticsFred Hutchinson Cancer Research CenterSeattleUSA
  4. 4.Department of RadiologyVirginia Mason Medical CenterSeattleUSA
  5. 5.Seattle Cancer Care AllianceSeattleUSA

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