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European Radiology

, Volume 22, Issue 2, pp 322–330 | Cite as

High resolution MRI of the breast at 3 T: which BI-RADS® descriptors are most strongly associated with the diagnosis of breast cancer?

  • K. Pinker-Domenig
  • W. Bogner
  • S. Gruber
  • H. Bickel
  • S. Duffy
  • M. Schernthaner
  • P. Dubsky
  • U. Pluschnig
  • M. Rudas
  • S. Trattnig
  • T. H. HelbichEmail author
Breast

Abstract

Objective

To identify which breast lesion descriptors in the ACR BI-RADS® MRI lexicon are most strongly associated with the diagnosis of breast cancer when performing breast MR imaging at 3 T.

Methods

150 patients underwent breast MR imaging at 3 T. Lesion size, morphology and enhancement kinetics were assessed according to the BI-RADS® classification. Sensitivity, specificity and diagnostic accuracy were assessed. The effects of the BI-RADS® descriptors on sensitivity and specificity were evaluated. Data were analysed using logistic regression. Histopathological diagnoses were used as the standard of reference.

Results

The sensitivity, specificity and diagnostic accuracy of breast MRI at 3 T was 99%, 81% and 93%, respectively. In univariate analysis, the final diagnosis of malignancy was positively associated with irregular shape (p < 0.001), irregular margin (p < 0.001), heterogeneous enhancement (p < 0.001), Type 3 enhancement kinetics (p = 0.02), increasing patient age (p = 0.02) and larger lesion size (p < 0.001). In multivariate analysis, significant associations with malignancy remained for mass shape (p = 0.06), mass margin (p < 0.001), internal enhancement pattern (p = 0.03) and Type 3 enhancement kinetics (p = 0.06).

Conclusion

The ACR BI-RADS® breast lesion descriptors that are mostly strongly associated with breast cancer in breast MR imaging at 3 T are lesion shape, lesion margin, internal enhancement pattern and Type 3 enhancement kinetics.

Key Points

• 3 Tesla breast MRI allows an accurate diagnosis of breast cancer

• The BI-RADS® descriptors help provide a confident diagnosis

• The shape, margin, enhancement pattern and kinetics are the most important features

• An irregular shape and margin, heterogeneous enhancement and type-3 kinetics indicate malignancy

Keywords

BI-RADS® 3 Tesla MRI Breast cancer Lesion descriptors 

References

  1. 1.
    Helbich TH (2000) Contrast-enhanced magnetic resonance imaging of the breast. Eur J Radiol 34(3):208–219PubMedCrossRefGoogle Scholar
  2. 2.
    Kuhl C (2007) The current status of breast MR imaging - Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 244(2):356–378PubMedCrossRefGoogle Scholar
  3. 3.
    Kuhl CK (2007) Current status of breast MR imaging. Part 2. Clinical applications. Radiology 244(3):672–691PubMedCrossRefGoogle Scholar
  4. 4.
    Kinkel K, Helbich TH, Esserman LJ et al (2000) Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: diagnostic criteria and interobserver variability. AJR Am J Roentgenol 175(1):35–43PubMedGoogle Scholar
  5. 5.
    Liberman L, Morris EA, Lee MJ et al (2002) Breast lesions detected on MR imaging: features and positive predictive value. AJR Am J Roentgenol 179(1):171–178PubMedGoogle Scholar
  6. 6.
    Vomweg TW, Teifke A, Schreiber WG, Schmidt M, Thelen M (2002) Combination of low and high resolution T1-weighted sequences for improved evaluation of morphologic criteria in dynamic contrast enhanced MRI of the breast. Rofo 174(11):1445–1449PubMedCrossRefGoogle Scholar
  7. 7.
    Pinker K, Grabner G, Bogner W et al (2009) A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 44(9):553–558PubMedCrossRefGoogle Scholar
  8. 8.
    Kuhl CK, Jost P, Morakkabati N, Zivanovic O, Schild HH, Gieseke J (2006) Contrast-enhanced MR imaging of the breast at 3.0 and 1.5 T in the same patients: initial experience. Radiology 239(3):666–676PubMedCrossRefGoogle Scholar
  9. 9.
    Kuhl CK (2007) Breast MR imaging at 3T. Magn Reson Imaging Clin N Am 15(3):315–320, viPubMedCrossRefGoogle Scholar
  10. 10.
    Noebauer-Huhmann IM, Pinker K, Barth M et al (2006) Contrast-enhanced, high-resolution, susceptibility-weighted magnetic resonance imaging of the brain: dose-dependent optimization at 3 tesla and 1.5 tesla in healthy volunteers. Invest Radiol 41(3):249–255PubMedCrossRefGoogle Scholar
  11. 11.
    Pinker K, Ba-Ssalamah A, Wolfsberger S, Mlynarik V, Knosp E, Trattnig S (2005) The value of high-field MRI (3 T) in the assessment of sellar lesions. Eur J Radiol 54(3):327–334PubMedCrossRefGoogle Scholar
  12. 12.
    Ba-Ssalamah A, Nobauer-Huhmann IM, Pinker K et al (2003) Effect of contrast dose and field strength in the magnetic resonance detection of brain metastases. Invest Radiol 38(7):415–422PubMedGoogle Scholar
  13. 13.
    Schmitz AC, Peters NH, Veldhuis WB et al (2008) Contrast-enhanced 3.0-T breast MRI for characterization of breast lesions: increased specificity by using vascular maps. Eur Radiol 18(2):355–364PubMedCrossRefGoogle Scholar
  14. 14.
    Mann RM, Kuhl CK, Kinkel K, Boetes C (2008) Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol 18(7):1307–1318PubMedCrossRefGoogle Scholar
  15. 15.
    Elsamaloty H, Elzawawi MS, Mohammad S, Herial N (2009) Increasing accuracy of detection of breast cancer with 3-T MRI. AJR Am J Roentgenol 192(4):1142–1148PubMedCrossRefGoogle Scholar
  16. 16.
    Radiology RVACo (2003) American College of Radiology (ACR). Breast Imaging and Reporting Data System (BI-RADS). (4th ed.)Google Scholar
  17. 17.
    Demartini WB, Kurland BF, Gutierrez RL, Blackmore CC, Peacock S, Lehman CD (2011) Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics. Eur Radiol 21(8):1609–1617PubMedCrossRefGoogle Scholar
  18. 18.
    Gutierrez RL, DeMartini WB, Eby PR, Kurland BF, Peacock S, Lehman CD (2009) BI-RADS lesion characteristics predict likelihood of malignancy in breast MRI for masses but not for nonmasslike enhancement. AJR Am J Roentgenol 193(4):994–1000PubMedCrossRefGoogle Scholar
  19. 19.
    Schnall MD, Blume J, Bluemke DA et al (2006) Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 238(1):42–53PubMedCrossRefGoogle Scholar
  20. 20.
    Collins DHC, Peters TM, Evans AC (1995) Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp 3(3):190–208CrossRefGoogle Scholar
  21. 21.
    Morris EA (2001) Illustrated breast MR lexicon. Semin Roentgenol 36(3):238–249PubMedCrossRefGoogle Scholar
  22. 22.
    Morris EA (2001) Review of breast MRI: indications and limitations. Semin Roentgenol 36(3):226–237PubMedCrossRefGoogle Scholar
  23. 23.
    Kuhl CK, Mielcareck P, Klaschik S et al (1999) Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211(1):101–110PubMedGoogle Scholar
  24. 24.
    Nunes LW, Schnall MD, Orel SG (2001) Update of breast MR imaging architectural interpretation model. Radiology 219(2):484–494PubMedGoogle Scholar
  25. 25.
    Nunes LW, Schnall MD, Orel SG et al (1997) Breast MR imaging: interpretation model. Radiology 202(3):833–841PubMedGoogle Scholar
  26. 26.
    Kuhl CK, Schild HH, Morakkabati N (2005) Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. Radiology 236(3):789–800PubMedCrossRefGoogle Scholar
  27. 27.
    Agrawal G, Su MY, Nalcioglu O, Feig SA, Chen JH (2009) Significance of breast lesion descriptors in the ACR BI-RADS MRI lexicon. Cancer 115(7):1363–1380PubMedCrossRefGoogle Scholar
  28. 28.
    Tardivon AA, Athanasiou A, Thibault F, El Khoury C (2007) Breast imaging and reporting data system (BI-RADS): magnetic resonance imaging. Eur J Radiol 61(2):212–215PubMedCrossRefGoogle Scholar
  29. 29.
    Floery D, Helbich TH (2006) MRI-Guided percutaneous biopsy of breast lesions: materials, techniques, success rates, and management in patients with suspected radiologic-pathologic mismatch. Magn Reson Imaging Clin N Am 14(3):411–425, viiiPubMedCrossRefGoogle Scholar
  30. 30.
    Schueller G, Jaromi S, Ponhold L et al (2008) US-guided 14-gauge core-needle breast biopsy: results of a validation study in 1352 cases. Radiology 248(2):406–413PubMedCrossRefGoogle Scholar
  31. 31.
    Kluttig A, Trocchi P, Heinig A et al (2007) Reliability and validity of needle biopsy evaluation of breast-abnormalities using the B-categorization–design and objectives of the Diagnosis Optimisation Study (DIOS). BMC Cancer 7:100PubMedCrossRefGoogle Scholar
  32. 32.
    Pathologists RCo (2001) NHS Cancer Screening Programes: Guidelinesfor non-operative diagnostic procedures and reporting in breast cancer screening(ed)^(eds). NSHBSP publication, SheffieldGoogle Scholar
  33. 33.
    Pathology EWGoBS (2006) Quality assurance guidelines for pathologyEuropean guidelines for quality assurance in cancer screening and diagnosis, 4th edn. European Union, pp 219–312Google Scholar
  34. 34.
    Gutierrez RL, Demartini WB, Eby P, Kurland BF, Peacock S, Lehman CD (2009) Clinical indication and patient age predict likelihood of malignancy in suspicious breast MRI lesions. Acad Radiol 16(10):1281–1285PubMedCrossRefGoogle Scholar
  35. 35.
    Baltzer PA, Benndorf M, Dietzel M, Gajda M, Runnebaum IB, Kaiser WA (2010) False-positive findings at contrast-enhanced breast MRI: a BI-RADS descriptor study. AJR Am J Roentgenol 194(6):1658–1663PubMedCrossRefGoogle Scholar
  36. 36.
    Liberman L, Mason G, Morris EA, Dershaw DD (2006) Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size. AJR Am J Roentgenol 186(2):426–430PubMedCrossRefGoogle Scholar
  37. 37.
    Wang LC, DeMartini WB, Partridge SC, Peacock S, Lehman CD (2009) MRI-detected suspicious breast lesions: predictive values of kinetic features measured by computer-aided evaluation. AJR Am J Roentgenol 193(3):826–831PubMedCrossRefGoogle Scholar
  38. 38.
    Boetes C, Veltman J, van Die L, Bult P, Wobbes T, Barentsz JO (2004) The role of MRI in invasive lobular carcinoma. Breast Cancer Res Treat 86(1):31–37PubMedCrossRefGoogle Scholar
  39. 39.
    Kuhl CK (2009) Why do purely intraductal cancers enhance on breast MR images? Radiology 253(2):281–283PubMedCrossRefGoogle Scholar
  40. 40.
    Goto M, Ito H, Akazawa K et al (2007) Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphologic features. J Magn Reson Imaging 25(1):104–112PubMedCrossRefGoogle Scholar
  41. 41.
    Pinker K, Stadlbauer A, Bogner W, Gruber S, Helbich TH (2010) Molecular imaging of cancer: MR spectroscopy and beyond. Eur J RadiolGoogle Scholar
  42. 42.
    Bogner WPK, Gruber S, Grabner G, Stadlbauer A, Weber M, Moser E, Helbich TH, Trattnig S (2009) Diffusion-weighted MRI for differentiation of breast lesions at 3.0 Tesla: How does selection of diffusion schemes affect diagnosis? Radiology 253(2):341–351PubMedCrossRefGoogle Scholar
  43. 43.
    Pinker K, Brader P, Karanikas G et al (2010) Functional and molecular imaging of breast tumors. Radiologe 50(11):1030–1038PubMedCrossRefGoogle Scholar
  44. 44.
    Gruber S., Debski B, Pinker K, et al. Three dimensional proton magnetic resonance spectroscopic imaging (3D-MRSI) for differentiation of benign and malignant breast lesions at 3 Tesla. RadiologyGoogle Scholar
  45. 45.
    Kuhl CK, Schrading S, Bieling HB et al (2007) MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet 370(9586):485–492PubMedCrossRefGoogle Scholar
  46. 46.
    Diekmann F, Diekmann S, Beljavskaja M et al (2004) Preoperative MRT of the breast in invasive lobular carcinoma in comparison with invasive ductal carcinoma. Rofo 176(4):544–549PubMedCrossRefGoogle Scholar
  47. 47.
    Dietzel M, Baltzer PA, Vag T et al (2010) Magnetic resonance mammography of invasive lobular versus ductal carcinoma: systematic comparison of 811 patients reveals high diagnostic accuracy irrespective of typing. J Comput Assist Tomogr 34(4):587–595PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2011

Authors and Affiliations

  • K. Pinker-Domenig
    • 1
  • W. Bogner
    • 2
    • 3
  • S. Gruber
    • 2
    • 3
  • H. Bickel
    • 1
    • 3
  • S. Duffy
    • 4
  • M. Schernthaner
    • 3
  • P. Dubsky
    • 5
  • U. Pluschnig
    • 6
  • M. Rudas
    • 7
  • S. Trattnig
    • 2
  • T. H. Helbich
    • 1
    Email author
  1. 1.Dept. of Radiology, Division of Molecular and Gender ImagingMedical University ViennaViennaAustria
  2. 2.Dept. of Radiology, MR Centre of ExcellenceMedical University ViennaViennaAustria
  3. 3.Dept. of RadiologyMedical University ViennaViennaAustria
  4. 4.Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and DentistryQueen Mary University of LondonLondonUK
  5. 5.Dept. of SurgeryMedical University ViennaViennaAustria
  6. 6.Dept. of Internal Medicine, Division of OncologyMedical University ViennaViennaAustria
  7. 7.Clinical Institute of PathologyMedical University ViennaViennaAustria

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