Department of Radiology, Charité University Medicine Berlin, Berlin, Germany.
J Magn Reson Imaging. 2012 May;35(5):1077-88. doi: 10.1002/jmri.23516. Epub 2012 Jan 13.
To evaluate a fully automatic computer-assisted diagnosis (CAD) method for breast magnetic resonance imaging (MRI), which considered dynamic as well as morphologic parameters and linked those to descriptions laid down in the Breast Imaging Reporting and Data System (BI-RADS) MRI atlas.
MR images of 108 patients with 141 histologically proven mass-like lesions (88 malignant, 53 benign) were included. The CAD system automatically performed the following processing steps: 3D nonrigid motion correction, detection of lesions by a segmentation algorithm, extraction of multiple dynamic and morphologic parameters, and classification of lesions. As one final result, the lesions were categorized by defining their probability of malignancy; this so-called morpho-dynamic index (MDI) ranged from 0%-100%. The results of the CAD system were correlated with histopathologic findings.
The CAD system had a high detection rate of the histologically proven lesions, missing only two malignancies of invasive multifocal carcinomas and four benign lesions (three fibroadenomas, one atypical ductal hyperplasia). The 86 detected malignant lesions showed a mean MDI of 86.1% (± 15.4%); the mean MDI of the 49 coded benign lesions was 41.8% (± 22.0%; P < 0.001). Based on receiver-operating characteristic analysis, the diagnostic accuracy of the CAD system was 93.5%. Using an appropriate cutoff value (MDI 50%), sensitivity was 96.5% combined with specificity of 75.5%.
The fully automatic CAD technique seems to reliably distinguish between benign and malignant mass-like breast tumors. Observer-independent CAD may be a promising additional tool for the interpretation of breast MRI in the clinical routine.
评估一种完全自动的计算机辅助诊断（CAD）方法，用于乳腺磁共振成像（MRI），该方法考虑了动态和形态学参数，并将这些参数与乳腺成像报告和数据系统（BI-RADS）MRI 图谱中的描述联系起来。
本研究纳入了 108 例患者的 141 个经组织学证实的肿块样病变（88 例恶性，53 例良性）的 MRI 图像。CAD 系统自动执行以下处理步骤：3D 非刚性运动校正、通过分割算法检测病变、提取多个动态和形态学参数以及病变分类。作为最终结果，通过定义病变的恶性概率对病变进行分类；这个所谓的形态动力学指数（MDI）范围从 0%-100%。CAD 系统的结果与组织病理学发现相关联。
该 CAD 系统对经组织学证实的病变具有较高的检出率，仅漏诊了 2 例浸润性多灶性癌和 4 例良性病变（3 例纤维腺瘤，1 例非典型导管增生）。86 个检测到的恶性病变的平均 MDI 为 86.1%（±15.4%）；49 个编码的良性病变的平均 MDI 为 41.8%（±22.0%；P<0.001）。基于受试者工作特征分析，CAD 系统的诊断准确性为 93.5%。使用适当的截断值（MDI 50%），敏感性为 96.5%，特异性为 75.5%。
全自动 CAD 技术似乎能够可靠地区分良性和恶性乳腺肿块样肿瘤。基于观察者的 CAD 可能是一种有前途的额外工具，可用于在临床常规中解释乳腺 MRI。
