PURPOSE: To both develop and use a tissue signature method for the identification and classification of breast lesions and healthy breast tissue with magnetic resonance (MR) imaging. MATERIALS AND METHODS: Thirty-six patients underwent breast MR imaging (T1- and T2-weighted imaging and three-dimensional T1-weighted imaging with and without contrast material enhancement), followed by biopsy or mastectomy and histopathologic analysis. Tissue cluster analysis was performed by using the iterative self-organizing data technique to identify glandular, adipose, and lesion tissue signature vectors. Glandular and lesion tissue vectors were characterized by angular separation from the reference adipose tissue vector. Differences in angular separation of histologically proved benign and malignant lesion groups were evaluated with an independent t test. The usefulness of the angular separation model for distinguishing benign from malignant lesions was evaluated with nonparametric receiver operating characteristic curve analysis. RESULTS: The model enabled successful identification and characterization of breast lesion tissue clusters in all patients; 18 lesions were benign, and 18 were malignant. Angular separation +/- SD was 17.8 degrees +/- 6.1 degrees between adipose tissue and malignant lesions and 29.0 degrees +/- 11.2 degrees between adipose tissue and benign lesions. Angular separations of benign lesions and malignant lesions were significantly different (P =.002), with a specificity of 78% and sensitivity of 89% at a cutoff value of 21 degrees. Significant differences in angular separation from adipose tissue also were found between glandular tissue and lesion tissue (P <.001) and, in glandular tissue, between patients with benign lesions and those with malignant lesions (P =.04). The area under the receiver operating characteristic curve was 0.84. CONCLUSION: Multispectral analysis of conventional breast MR images based on the iterative self-organizing data model and on measurement of angular separation between tissue signature vectors may enable automated lesion identification and classification. Copyright RSNA, 2003
PURPOSE: To both develop and use a tissue signature method for the identification and classification of breast lesions and healthy breast tissue with magnetic resonance (MR) imaging. MATERIALS AND METHODS: Thirty-six patients underwent breast MR imaging (T1- and T2-weighted imaging and three-dimensional T1-weighted imaging with and without contrast material enhancement), followed by biopsy or mastectomy and histopathologic analysis. Tissue cluster analysis was performed by using the iterative self-organizing data technique to identify glandular, adipose, and lesion tissue signature vectors. Glandular and lesion tissue vectors were characterized by angular separation from the reference adipose tissue vector. Differences in angular separation of histologically proved benign and malignant lesion groups were evaluated with an independent t test. The usefulness of the angular separation model for distinguishing benign from malignant lesions was evaluated with nonparametric receiver operating characteristic curve analysis. RESULTS: The model enabled successful identification and characterization of breast lesion tissue clusters in all patients; 18 lesions were benign, and 18 were malignant. Angular separation +/- SD was 17.8 degrees +/- 6.1 degrees between adipose tissue and malignant lesions and 29.0 degrees +/- 11.2 degrees between adipose tissue and benign lesions. Angular separations of benign lesions and malignant lesions were significantly different (P =.002), with a specificity of 78% and sensitivity of 89% at a cutoff value of 21 degrees. Significant differences in angular separation from adipose tissue also were found between glandular tissue and lesion tissue (P <.001) and, in glandular tissue, between patients with benign lesions and those with malignant lesions (P =.04). The area under the receiver operating characteristic curve was 0.84. CONCLUSION: Multispectral analysis of conventional breast MR images based on the iterative self-organizing data model and on measurement of angular separation between tissue signature vectors may enable automated lesion identification and classification. Copyright RSNA, 2003
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