Department of Medical Biophysics, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Room S605, Toronto, ON M4N 3M5, Canada.
Acad Radiol. 2011 Dec;18(12):1577-81. doi: 10.1016/j.acra.2011.08.004. Epub 2011 Sep 29.
Cancer screening by magnetic resonance imaging (MRI) has been shown to be one of the most sensitive methods available for the early detection of breast cancer. There is high variability in the diagnostic accuracy of radiologists analyzing the large amounts of data acquired in a breast MRI examination, and this has motivated substantial research toward the development of computer-aided detection and diagnosis systems. Most computer-aided diagnosis systems for breast MRI focus on dynamic information (how a lesion's brightness changes over the course of an examination after the injection of a contrast agent). The inclusion of lesion margin measurements is much less common. One characteristic of malignant tumors is that they grow into neighboring tissues. This growth creates tumor margins that are variably fuzzy or diffuse (ie, they are not sharp).
In this short report, the authors present a new method for measuring a tumor's margin from breast MRI examinations and compare it with an existing mathematical technique for margin measurements.
The proposed method can yield a test with sensitivity of 77% (specificity, 65%) on screening data, outperforming existing mathematical lesion margin measurement methods. Furthermore, when the presented margin measurement is combined with existing dynamic features, there is a statistically significant improvement in computer-aided diagnosis test performance (P < .0014).
The proposed method for measuring a tumor's margin outperforms existing mathematical methods on an extremely challenging data set containing many small lesions. The technique presented may be useful in discriminating between malignant and benign lesions in the context of the computer-aided diagnosis of breast cancer from MRI.
磁共振成像（MRI）的癌症筛查已被证明是用于早期发现乳腺癌的最敏感方法之一。分析乳腺 MRI 检查中获取的大量数据的放射科医生的诊断准确性存在很大差异，这促使人们大力研究开发计算机辅助检测和诊断系统。大多数用于乳腺 MRI 的计算机辅助诊断系统都集中在动态信息上（在注射造影剂后，病变的亮度在检查过程中如何变化）。病变边缘测量的纳入要少见得多。恶性肿瘤的一个特征是它们会生长到邻近的组织中。这种生长会导致肿瘤边缘变得模糊或扩散（即，不清晰）。
在这份简短的报告中，作者提出了一种从乳腺 MRI 检查中测量肿瘤边缘的新方法，并将其与现有的边缘测量数学技术进行了比较。
该方法在筛查数据上的测试灵敏度为 77%（特异性为 65%），优于现有的数学病变边缘测量方法。此外，当呈现的边缘测量与现有的动态特征相结合时，计算机辅助诊断测试性能有显著提高（P<0.0014）。
在包含许多小病变的极具挑战性的数据集上，用于测量肿瘤边缘的新方法优于现有的数学方法。该技术在利用 MRI 进行乳腺癌计算机辅助诊断时，可能有助于区分恶性和良性病变。
