Combined Use of T2-Weighted MRI and T1-Weighted Dynamic Contrast-Enhanced MRI in the Automated Analysis of Breast Lesions

A multiparametric computer-aided diagnosis scheme that combines information from T-1-weighted dynamic contrast-enhanced (DCE)-MRI and T-2-weighted MRI was investigated using a database of 110 malignant and 86 benign breast lesions. Automatic lesion segmentation was performed, and three categories of lesion features (geometric, T-1-weighted DCE, and T-2-weighted) were automatically extracted. Step-wise feature selection was performed considering only geometric features, only T-1-weighted DCE features, only T-2-weighted features, and all features. Features were merged with Bayesian artificial neural networks, and diagnostic performance was evaluated by ROC analysis. With leave-one-lesion-out cross-validation, an area under the ROC curve value of 0.77 +/- 0.03 was achieved with T-2-weighted-only features, indicating high diagnostic value of information in T-2-weighted images. Area under the ROC curve values of 0.79 +/- 0.03 and 0.80 +/- 0.03 were obtained for geometric-only features and T-1-weighted DCE-only features, respectively. When all features were considered, an area under the ROC curve value of 0.85 +/- 0.03 was achieved. We observed P values of 0.006, 0.023, and 0.0014 between the geometric-only, T-1-weighted DCE-only, and T-2-weighted-only features and all features conditions, respectively. When ranked, the P values satisfied the Holm-Bonferroni multiple-comparison test; thus, the improvement of multiparametric computer-aided diagnosis was statistically significant. A computer-aided diagnosis scheme that combines information from T-1-weighted DCE and T-2-weighted MRI may be advantageous over conventional T-1-weighted DCE-MRI computer-aided diagnosis. Magn Reson Med 66:555-563, 2011. (C) 2011 Wiley-Liss, Inc.