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Benign and Malignant Breast Lesions: Diagnosis with Multiparametric MR Imaging¹

¹ From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Traylor Bldg, Room 217, 712 Rutland Ave, Baltimore, MD 21205 (M.A.J., P.B.B., D.A.B., C.M., C.A., E.H.H., Z.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (P.B.B.). Received March 22, 2002; revision requested June 11; final revision received January 13, 2003; accepted February 24. M.A.J. supported by NIH T32 CA09630 and by Breast Cancer Spore P50 CA88843. P.B.B. supported by NIH R21CA/RR91798. D.A.B. supported by NIH RFA CA96012. Address correspondence to M.A.J. (e-mail: mikej@mri.jhu.edu).

View larger version (47K): [in a new window]   Figure 1. Representative sagittal MR image data set analyzed with the ISODATA algorithm. A, T1-weighted image acquired with a fast spoiled gradient-echo pulse sequence (200/4.4). B, Fat-suppressed T2-weighted image acquired with a spin-echo pulse sequence (5,700/102). C, Three-dimensional fat-suppressed T1-weighted image acquired with a fast spoiled gradient-echo pulse sequence (20/4) prior to contrast enhancement by gadodiamide. D, Contrast-enhanced three-dimensional T1-weighted image acquired with the same pulse sequence as in C.

View larger version (65K): [in a new window]   Figure 2. MR image data from the same patient as in Figure 1. A, Diagram of signature vectors defined for each normal tissue type (adipose and glandular) and assigned to the tissue cluster that most closely resembles the vector elements for that tissue type in the ISODATA algorithm. B, Representative ISODATA theme map of the signature vectors defined for each tissue cluster shows normal adipose tissue (dark blue) in most of the breast. Regions that are light blue, green, and yellow represent glandular tissue, and pink and white areas represent tumor tissue. C, Diagram of the three-dimensional feature space formed by the combination of T1-weighted images and T2-weighted fat-suppressed images with contrast-enhanced three-dimensional images. The angular separation model, with the distribution of tissue clusters in the three-dimensional feature space, is shown. Angles were calculated as the dot product between the normal tissue cluster and the abnormal tissue cluster by using each cluster’s tissue signature vector (1, 2). Each axis represents the signal intensity distribution for each MR image type.

View larger version (76K): [in a new window]   Figure 3. MR image data from a 56-year-old woman with infiltrating ductal carcinoma confirmed by histologic analysis following mastectomy. A, Theme map obtained with multiparametric ISODATA segmentation at an angular separation threshold of 19° demonstrates clear delineation of adipose tissue (blue) from glandular tissue (light green to yellow). B, Sagittal T1-weighted contrast-enhanced digital subtraction image acquired with a FSPGR pulse sequence (20/4). C, D, Histologic photomicrographs (hematoxylin-eosin stain, original magnification in C, x2; in D, x40) on which the tumor tissue appears in pink and white. The histologic morphology of the lesion was consistent with that of infiltrating ductal carcinoma.

View larger version (24K): [in a new window]   Figure 4. Graph of ROC curves for ISODATA-based analysis of angular separation according to lesion tissue type and for all tissues. The area under the curve for lesion tissue type was 0.84. The diagonal line indicates an area under the curve of 0.50 (ie, no separation between tissue types).