Computerized Assessment of Breast Lesion Malignancy using DCE-MRI

Weijie Chen, Maryellen L. Giger, Gillian M. Newstead, Ulrich Bick, Sanaz A. Jansen, Hui Li, Li Lan
2010 Academic Radiology  
Rationale and Objectives-To conduct a pre-clinical evaluation of the robustness of our computerized system for breast lesion characterization on two breast magnetic resonance imaging (MRI) databases that were acquired using scanners from two different manufacturers. Materials and Methods-Two clinical breast MRI databases were acquired from a Siemens scanner and a GE scanner, which shared similar imaging protocols and retrospectively collected under an IRB-approved protocol. In our computerized
more » ... nalysis system, once a breast lesion is identified by the radiologist, the computer performs automatic lesion segmentation and feature extraction, and outputs an estimated probability of malignancy. We used a Bayesian neural network with automatic relevance determination for joint feature selection and classification. To evaluate the robustness of our classification system, we first used Database 1 for feature selection and classifier training, and Database 2 to test the trained classifier. Then, we exchanged the two datasets and repeated the process. Area under the ROC curve (AUC) was used as a performance figure of merit in the task of distinguishing between malignant and benign lesions. Results-We obtained an AUC of 0.85 (approximate 95% confidence interval (CI): [0.79, 0.91]) for (a) feature selection and classifier training using Database 1 and testing on Database 2; and an AUC of 0.90 (approximate 95% CI: [0.84, 0.96]) for (b) feature selection and classifier training using Database2 and testing on Database1. We failed to observe statistical significance for the difference AUC of 0.05 between the two database-conditions (P=0.24; 95% confidence interval [− 0.03, 0.1]). Conclusion-These results demonstrate the robustness of our computerized classification system in the task of distinguishing between malignant and benign breast lesions on DCE-MRI images from two manufacturers. Our study showed the feasibility of developing a computerized classification system that is robust across different scanners.
doi:10.1016/j.acra.2010.03.007 pmid:20540907 pmcid:PMC2907891 fatcat:2s3ohvyrejd45k6e2o56aoflzu