Gilhuijs K G, Giger M L, Bick U
Kurt Rossmann Laboratories for Radiologic Image Research, University of Chicago, Department of Radiology, Illinois 60637, USA.
Med Phys. 1998 Sep;25(9):1647-54. doi: 10.1118/1.598345.
Contrast-enhanced magnetic resonance imaging (MRI) of the breast is known to reveal breast cancer with higher sensitivity than mammography alone. The specificity is, however, compromised by the observation that several benign masses take up contrast agent in addition to malignant lesions. The aim of this study is to increase the objectivity of breast cancer diagnosis in contrast-enhanced MRI by developing automated methods for computer-aided diagnosis. Our database consists of 27 MR studies from 27 patients. In each study, at least four MR series of both breasts are obtained using FLASH three-dimensional (3D) acquisition at 90 s time intervals after injection of Gadopentetate dimeglumine (Gd-DTPA) contrast agent. Each series consists of 64 coronal slices with a typical thickness of 2 mm, and a pixel size of 1.25 mm. The study contains 13 benign and 15 malignant lesions from which features are automatically extracted in 3D. These features include margin descriptors and radial gradient analysis as a function of time and space. Stepwise multiple regression is employed to obtain an effective subset of combined features. A final estimate of likelihood of malignancy is determined by linear discriminant analysis, and the performance of classification by round-robin testing and receiver operating characteristics (ROC) analysis. To assess the efficacy of 3D analysis, the study is repeated in two-dimensions (2D) using a representative slice through the middle of the lesion. In 2D and in 3D, radial gradient analysis and analysis of margin sharpness were found to be an effective combination to distinguish between benign and malignant masses (resulting area under the ROC curve: 0.96). Feature analysis in 3D was found to result in higher performance of lesion characterization than 2D feature analysis for the majority of single and combined features. In conclusion, automated feature extraction and classification has the potential to complement the interpretation of radiologists in an objective, consistent, and accurate way.
已知乳腺对比增强磁共振成像（MRI）检测乳腺癌的灵敏度高于单纯乳腺X线摄影。然而，其特异性受到影响，因为观察发现除恶性病变外，一些良性肿块也会摄取造影剂。本研究的目的是通过开发计算机辅助诊断的自动化方法，提高对比增强MRI中乳腺癌诊断的客观性。我们的数据库包含来自27名患者的27项MR研究。在每项研究中，在注射钆喷酸葡胺（Gd-DTPA）造影剂后，以90秒的时间间隔使用快速成像三维（3D）采集获得至少四个双侧乳腺的MR序列。每个序列由64个冠状面切片组成，典型厚度为2mm，像素大小为1.25mm。该研究包含13个良性病变和15个恶性病变，从中自动提取三维特征。这些特征包括边缘描述符以及作为时间和空间函数的径向梯度分析。采用逐步多元回归获得组合特征的有效子集。通过线性判别分析确定恶性可能性的最终估计值，并通过循环测试和受试者操作特征（ROC）分析评估分类性能。为了评估三维分析的有效性，使用穿过病变中部的代表性切片在二维（2D）中重复该研究。在二维和三维中，发现径向梯度分析和边缘清晰度分析是区分良性和恶性肿块的有效组合（ROC曲线下面积：0.96）。对于大多数单一和组合特征，发现三维特征分析比二维特征分析具有更高的病变特征化性能。总之，自动化特征提取和分类有潜力以客观、一致和准确的方式辅助放射科医生的解读。
