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.