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Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study¹

¹ From the Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, Pa 19104 (M.D.S.). Author affiliations: Center for Statistical Sciences, Brown University, Providence, RI (J.B., C.A.G.); Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Md (D.A.B.); Department of Radiology, University of Virginia Health System, Charlottesville, Va (G.A.D.); Department of Radiological Sciences, UCLA School of Medicine, Los Angeles, Calif (N.D.); Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, Ark (S.H.); Technical University Munich, Munich, Germany (S.H.H.); Magnetic Resonance Science Center, Department of Radiology, University of California, San Francisco, Calif (N.H.); Department of Radiology, University of Bonn, Bonn, Germany (C.K.K.); Department of Radiology, University of North Carolina Chapel Hill, Chapel Hill, NC (E.D.P.); University of Toronto-Sunnybrook Cancer Care Center, Toronto, Ontario, Canada (P.C.); Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Mass (S.J.S.); Radiology Imaging Associates, Porter Adventist Hospital, Denver, Colo (D.T.); Division of Diagnostic Imaging, The University of Texas M. D. Anderson Cancer Center, Houston, Tex (C.B.S.); Department of Radiology, University of Texas-Southwestern Medical Center, Dallas, Tex (P.T.W.); and University of Washington Medical Center, Seattle, Wash (C.L.). From the 2004 RSNA Annual Meeting. Received December 14, 2004; revision requested February 2, 2005; revision received March 10; final version accepted March 23. Supported by National Cancer Institute grants UO1-CA74696 and UO1-CA74680. Address correspondence to M.D.S. (e-mail: Mitchell.Schnall{at}uphs.upenn.edu).

Purpose: To prospectively determine the prevalence and predictive value of three-dimensional (3D) and dynamic breast magnetic resonance (MR) imaging and contrast material kinetic features alone and as part of predictive diagnostic models.

Materials and Methods: The study protocol was approved by the institutional review board or ethics committees of all participating institutions, and informed consent was obtained from all participants. Although study data collection was performed before HIPAA went into effect, standards that would be compliant with HIPAA were adhered to. Data from the International Breast MR Consortium trial 6883 were used in the analysis. Women underwent 3D (minimum spatial resolution, 0.7 x 1.4 x 3 mm; minimal temporal resolution, 4 minutes) and dynamic two-dimensional (temporal resolution, 15 seconds) MR imaging examinations. Readers rated enhancement shape, enhancement distribution, border architecture, enhancement intensity, presence of rim enhancement or internal septations, and the shape of the contrast material kinetic curve. Regression was performed for each feature individually and after adjustment for associated mammographic findings. Multivariate models were also constructed from multiple architectural and dynamic features. Areas under the receiver operating characteristic curve (A_z values) were estimated for all models.

Results: There were 995 lesions in 854 women (mean age, 53 years ± 12 [standard deviation]; range, 18–80 years) for whom pathology data were available. The absence of enhancement was associated with an 88% negative predictive value for cancer. Qualitative characterization of the dynamic enhancement pattern was associated with an A_z value of 0.66 across all lesion architectures. Focal mass margins (A_z = 0.76) and signal intensity (A_z = 0.70) were highly predictive imaging features. Multivariate models were constructed with an A_z value of 0.880.

Conclusion: Architectural and dynamic features are important in breast MR imaging interpretation. Multivariate models involving feature assessment have a diagnostic accuracy superior to that of qualitative characterization of the dynamic enhancement pattern.

© RSNA, 2006

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