A multiparametric computer-aided diagnosis scheme that combines information from T1-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, 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 T 2-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 T 1-weighted DCE and T2-weighted MRI may be advantageous over conventional T1-weighted DCE-MRI computer-aided diagnosis.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging