Department of Radiology, The University of Chicago, IL 60637, USA.
Acad Radiol. 2010 Jul;17(7):822-9. doi: 10.1016/j.acra.2010.03.007.
To conduct a preclinical 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.
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 institutional review board-approved protocol. In our computerized analysis system, after 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 receiver operating characteristic curve (AUC) was used as a performance figure of merit in the task of distinguishing between malignant and benign lesions.
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 Database 2 and testing on Database 1. We failed to observe statistical significance for the difference AUC of 0.05 between the two database conditions (P = .24; 95% confidence interval -0.03, 0.1).
These results demonstrate the robustness of our computerized classification system in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced (DCE) MRI images from two manufacturers. Our study showed the feasibility of developing a computerized classification system that is robust across different scanners.
在使用来自两个不同制造商的扫描仪采集的两个乳房磁共振成像 (MRI) 数据库上，对我们用于乳房病变特征描述的计算机系统的稳健性进行临床前评估。
从西门子扫描仪和通用电气扫描仪采集了两个临床乳房 MRI 数据库，这两个扫描仪具有相似的成像协议，并根据机构审查委员会批准的方案进行了回顾性采集。在我们的计算机分析系统中，放射科医生识别出乳房病变后，计算机自动进行病变分割和特征提取，并输出恶性肿瘤的估计概率。我们使用带自动相关性确定的贝叶斯神经网络进行联合特征选择和分类。为了评估我们的分类系统的稳健性，我们首先使用数据库 1 进行特征选择和分类器训练，并使用数据库 2 测试训练后的分类器。然后，我们交换了两个数据集并重复了该过程。接收器工作特征曲线下的面积 (AUC) 是用于区分良恶性病变的性能度量。
我们获得了以下结果：(a) 使用数据库 1 进行特征选择和分类器训练，并在数据库 2 上进行测试，AUC 为 0.85（近似 95%置信区间 [CI] 0.79-0.91）；(b) 使用数据库 2 进行特征选择和分类器训练，并在数据库 1 上进行测试，AUC 为 0.90（近似 95%CI 0.84-0.96）。我们未观察到两种数据库条件下 AUC 差异 0.05 的统计学意义（P =.24；95%置信区间 -0.03，0.1）。
这些结果表明，我们的计算机分类系统在区分来自两个制造商的动态对比增强 (DCE) MRI 图像中的恶性和良性乳房病变方面具有稳健性。我们的研究表明，开发跨不同扫描仪稳健的计算机分类系统是可行的。
