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 T1‐weighted dynamic contrast–enhanced (DCE)‐MRI and T2‐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, T1‐weighted DCE, and T2‐weighted) were automatically extracted. Stepwise feature selection was performed considering only geometric features, only T1‐weighted DCE features, only T2‐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 T2‐weighted‐only features, indicating high diagnostic value of information in T2‐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 T1‐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, T1‐weighted DCE‐only, and T2‐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 T1‐weighted DCE and T2‐weighted MRI may be advantageous over conventional T1‐weighted DCE‐MRI computer‐aided diagnosis. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.