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

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Abstract

Objectives

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

Methods

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.

Results

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.

Conclusions

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

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Acknowledgements

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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Authors and Affiliations

  1. Department of Radiology, University of Washington Medical Center, 1959 NE Pacific, Seattle, WA, 98195, USA

    Wendy B. DeMartini, Robert L. Gutierrez, Sue Peacock & Constance D. Lehman

  2. Breast imaging, Seattle Cancer Care Alliance, 825 Eastlake Avenue East, G3-200, Seattle, WA, 98109–1023, USA

    Wendy B. DeMartini, Robert L. Gutierrez, Sue Peacock & Constance D. Lehman

  3. Clinical Statics, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, PO Box 19024, Seattle, WA, 98111, USA

    Brenda F. Kurland

  4. Department of Radiology, Virginia Mason Medical Center, 1100 9th Avenue, PO Box 900, Seattle, WA, 98111, USA

    C. Craig Blackmore

  5. Seattle Cancer Care Alliance, 825 Eastlake Avenue East, G3-200, Seattle, WA, 98109–1023, USA

    Brenda F. Kurland

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  1. Wendy B. DeMartini
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Correspondence to Wendy B. DeMartini.

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DeMartini, W.B., Kurland, B.F., Gutierrez, R.L. et al. Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics. Eur Radiol 21, 1609–1617 (2011). https://doi.org/10.1007/s00330-011-2094-6

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  • DOI: https://doi.org/10.1007/s00330-011-2094-6

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